@proceedings{ANNGA93, title = {Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms}, year = {1993}, month = {April 14-16}, editor = {Rudolf F. Albrecht and Colin R. Reeves and Nigel C. Steele}, publisher = {Springer-Verlag} } @proceedings{AlifeI, title = {Artificial Life: the Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems}, year = {1989}, month = {September}, editor = {Christopher G. Langton}, publisher = {Addison-Wesley}, address = {Redwood City, CA}, note = {Workshop held September, 1987 in Los Alamos, New Mexico} } @proceedings{AlifeII, title = {Artificial Life II: Proceedings of the Workshop on Artificial Life}, year = {1992}, editor = {Christopher G. Langton and Charles Taylor and J. Doyne Farmer and Steen Rasmussen}, publisher = {Addison-Wesley}, address = {Redwood City, Calif.}, note = {Workshop held February, 1990 in Santa Fe, New Mexico} } @proceedings{AlifeIII, title = {Artificial Life III: Proceedings of the Workshop on Artificial Life}, year = {1994}, editor = {Christopher G. Langton}, publisher = {Addison-Wesley}, address = {Reading, MA}, note = {Workshop held June, 1992 in Santa Fe, New Mexico} } @proceedings{COGANN92, title = {International Workshop on Combinations of Genetic Algorithms and Neural Networks: COGANN-92}, year = {1992}, editor = {L. D. Whitley and J. D. Schaffer}, publisher = {IEEE Computer Society Press}, address = {Los Alamiitos, California} } @proceedings{FOGA1, title = {Proceedings of the Workshop on Foundations of Genetic Algorithms}, year = {1991}, editor = {Gregory J. E. Rawlins}, publisher = {Morgan Kaufmann}, address = {San Mateo, California} } @proceedings{FOGA2, title = {Proceedings of the Workshop on Foundations of Genetic Algorithms}, year = {1993}, editor = {Darrell L. Whitley}, publisher = {Morgan Kaufmann}, address = {San Mateo, California}, note = {The second workshop on Foundations of Genetic Algorithms (FOGA) was held July 26-29, 1992 in Vail, Colorado} } @proceedings{ICGA85, title = {Proceedings of the First International Conference on Genetic Algorithms and their Applications}, year = {1985}, month = {July 24-26}, editor = {John J. Grefenstette}, publisher = {Lawrence Erlbaum Associates}, address = {Pittsburgh, Pa} } @proceedings{ICGA87, title = {Proceedings of the Second International Conference on Genetic Algorithms and their Applications}, organization = {Massachusetts Institute of Technology, Cambridge, MA}, year = {1987}, month = {July 28-31}, editor = {John J. Grefenstette}, publisher = {Lawrence Erlbaum Associates}, address = {Hillsdale, New Jersey} } @proceedings{ICGA89, title = {Proceedings of the Third International Conference on Genetic Algorithms}, organization = {George Mason University}, year = {1989}, month = {June 4-7}, editor = {J. David Schaffer}, publisher = {Morgan Kaufmann}, address = {San Mateo, California} } @proceedings{ICGA91, title = {Proceedings of the Fourth International Conference on Genetic Algorithms}, organization = {University of California, San Diego}, year = {1991}, month = {July 13-16}, editor = {Richard K. Belew and Lashon B. Booker}, publisher = {Morgan Kaufmann}, address = {San Mateo, CA} } @proceedings{ICGA93, title = {Proceedings of the Fifth International Conference on Genetic Algorithms}, organization = {University of Illinois at Urbana Champaign}, year = {1993}, month = {July 17-21}, editor = {Stephanie Forrest}, publisher = {Morgan Kaufmann}, address = {San Mateo, CA} } @proceedings{PPSN91, title = {Proceedings of the First Conference on Parallel Problem Solving from Nature}, year = {1991}, month = {October 1-3}, editor = {Hans-Paul Schwefel and Reinhart M{\"a}nner}, publisher = {Springer-Verlag}, address = {Dortmund, Germany}, volume = {496}, series = {Lecture Notes in Computer Science} } @proceedings{PPSN92, title = {Proceedings of the Second Conference on Parallel Problem Solving from Nature, Brussels, Belgium}, year = {1992}, month = {September 28-30}, editor = {Reinhart M{\"a}nner and Bernhard Manderick}, publisher = {Elsevier}, address = {Amsterdam} } @conference{Ackley85, key = {genetic algorithm, boltzmann, connectionism, cogann ref}, author = {David H. Ackley}, title = {A Connectionist Algorithm for Genetic Search}, booktitle = {Proceedings of the First International Conference on Genetic Algorithms and their Applications}, year = {1985}, editor = {John. J Grefenstette}, publisher = {Lawrence Erlbaum Associates}, address = {Hillsdale, New Jersey}, pages = {121-135}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @book{Ackley87, key = {connectionism, genetic algorithm, sigh, stochastic iterated, cogann ref}, author = {David H. Ackley}, title = {A Connectionist Machine for Genetic Hillclimbing}, year = {1987}, publisher = {Kluwer Academic Publishers}, address = {Boston, MA}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Ackley92, author = {David H. Ackley and Michael L. Littman}, title = {Interactions between Learning and Evolution}, booktitle = {Artificial Life II}, year = {1992}, editor = {Christopher G. Langton and Charles Taylor and J. Doyne Farmer and Steen Rasmussen}, publisher = {Addison}, pages = {487-509}, annote = {connectionism genetic algorithm neighborhood mate selection, cogann ref animat}, topology = {feed-forward}, network = { }, encoding = {direct}, evolves = {parameters, connectivity}, applications = {simulated world} } @inproceedings{Alba93, key = {genetic algorithms connectionism neural networks cogann}, author = {E. Alba and J.F. Aldana and J.M. Troya}, title = {Genetic Algorithms as Heuristics for Optimizing ANN Design}, booktitle = {Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {683-690}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @article{Angeline94, key = {genetic algorithms connectionism neural networks cogann programming}, author = {Peter J. Angeline and Gregory M. Saunders and Jordan B. Pollack}, title = {An Evolutionary Algorithm that Constructs Recurrent Neural Networks}, journal = {IEEE Transactions on Neural Networks}, year = {1994}, volume = {5}, pages = {54-64}, topology = {recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @article{Ankenbrandt90, key = {Connectionism, fuzzy logic, pattern recognition}, author = {C. A. Ankenbrandt and B. P. Buckles and F. E. Petry}, title = {Scene Recognition using Genetic Algorithms with Semantic Nets}, journal = {Pattern Recognition Letters}, year = {1990}, month = {April}, volume = {11}, pages = {285-293}, publisher = {North-Holland}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {image recognition} } @inproceedings{Arena93, key = {genetic algorithms connectionism neural networks cogann}, author = {P. Arena and R. Caponetto and I. Fortuna and M. G. Xibilia}, title = {MLP Optimal Topology via Genetic Algorithms}, booktitle = {Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {670-674}, topology = {multi-layered}, network = {multi-layer perceptron}, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Austin91, key = {algorithms connectionism, cogann ref}, author = {Scott Austin}, title = {Genetic Neurosynthesis}, booktitle = {Proceedings of AIAA Aerospace VIII}, year = {1991}, month = {October}, address = {Baltimore, MD}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Ball90, key = {connectionism, cogann ref}, author = {N. Ball}, title = {Adaptive Signal Processing via Genetic Algorithms and Self-organizing Neural Networks}, booktitle = {Proceedings of the IEEE Workshop on Genetic Algorithms, Simulated Annealing and Neural Networks}, year = {1990}, address = {University of Glasgow, Scotland}, network = {self-organizing}, encoding = { }, evolves = { }, applications = {signal processing} } @inproceedings{Ball93, key = {genetic algorithms connectionism neural networks cogann}, author = {N. R. Ball}, title = {Towards the Development of Cognitive Maps in Classifier Systems}, booktitle = {Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms}, year = {1993}, pages = {712-718}, topology = { }, network = { }, encoding = {indirect, classifier systems}, evolves = { }, applications = {cognitive maps} } @article{Beer92, key = {genetic algorithms GENESIS, connectionism}, author = {Randall D. Beer and John C. Gallagher}, title = {Evolving Dynamical Neural Networks for Adaptive Behavior}, journal = {Adaptive Behavior}, year = {1992}, volume = {1}, number = {1}, topology = {recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = {animat controller} } @conference{Belew89, key = {hybrid learning, connectionism, cogann ref}, author = {Richard K. Belew}, title = {When Both Individuals and Populations Search: Adding Simple Learning to the Genetic Algorithm}, booktitle = {Proceedings of the Third International Conference on Genetic Algorithms}, organization = {ICGA89}, year = {1989}, editor = {Schaffer, J. D}, publisher = {Morgan Kaufmann}, pages = {34-41}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Belew89a, key = {connectionism, genetic algorithms, cogann ref}, author = {Richard K. Belew}, title = {Evolution, Learning and Culture: Computational Metaphors for Adaptive Algorithms}, institution = {University of California at San Deigo}, year = {1989}, month = {September}, address = {La Jolla, CA}, type = {CSE Technical Report CS89-156}, publisher = {University of California at San Deigo}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Belew89b, key = {connectionism, cogann ref}, author = {Richard K. Belew and John McInerney}, title = {Using the Genetic Algorithm to Wire Feed-forward Networks}, institution = {University of California, San Diego}, year = {1989}, month = {May}, address = {La Jolla, CA}, type = {Technical abstract}, note = {Submitted to Neural Information Processing Systems 1989}, publisher = {Computer Science \& Engineering Dept., University of California at San Deigo}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @techreport{Belew90, key = {neural nets, cogann ref}, author = {Richard K. Belew and John McInerney and Nicol N. Schraudolph}, title = {Evolving Networks: Using Genetic Algorithms with Connectionist Learning}, institution = {University of California at San Diego}, year = {1990}, month = {June}, address = {La Jolla, CA}, type = {CSE Technical Report CS90-174}, publisher = {University of California at San Diego}, topology = {feed-forward}, network = { }, encoding = {direct, developmental}, evolves = {parameters}, applications = { } } @inproceedings{Bengio91, key = {genetic algorithms connectionism neural networks cogann}, author = {Yoshua Bengio and Samy Bengio and Jocelyn Cloutier}, title = {Learning a synaptic learning rule}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1991}, pages = {969}, abstract = {ABSTRACT Summary form only given, as follows. The Authors discuss original approach to neural modeling based on the idea of searching, with learning methods, for a synaptic learning rule which is biologically plausible and yields networks that are able to learn to perform difficult tasks. The proposed method of automatically finding the learning rule relies on the idea of considering the synaptic modification rule as a parametric function. This function has local inputs and is the same in many neurons. The parameters that define this function can be estimated with known learning methods. For this optimization, particular attention is given to gradient descent and genetic algorithms. In both cases, estimation of this function consists of a joint global optimization of the synaptic modification function and the networks that are learning to perform some tasks. Both network architecture and the learning function can be designed within constraints derived from biological knowledge.}, topology = { }, network = { }, encoding = { }, evolves = {learning rule}, applications = { } } @article{Bergman88, author = {Aviv Bergman}, title = {Variation and Selection: An Evolutionary Model of Learning in Neural Networks}, journal = {Neural Networks}, year = {1988}, volume = {1}, number = {1}, pages = {75-}, network = { }, encoding = { }, applications = { } } @inproceedings{Bergman89, key = {genetic algorithms, connectionism recurrent neural networks, cogann ref}, author = {Aviv Bergman}, title = {Self-Organization by Simulated Evolution}, booktitle = {Lectures in Complex Systems: Proceedings of the 1989 Complex Systems Summer School}, year = {1989}, editor = {E. Jen}, address = {Santa Fe}, network = {self-organizing}, encoding = { }, evolves = { }, applications = { } } @inproceedings{Bergman87, key = {genetic algorithms, connectionism, evolution, cogann ref}, author = {Aviv Bergman and Michel Kerszberg}, title = {Breeding Intelligent Automata}, booktitle = {Proceedings of IEEE Conference on Neural Networks}, year = {1987}, month = {June 21-24}, address = {San Diego, CA}, pages = {63-70}, volume = {II}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Bessiere92, key = {genetic algorithms connectionism neural networks cogann}, author = {P. Bessiere}, title = {Genetic Algorithms Applied to Formal Neural Networks: Parallel Genetic Implementation of a Boltzmann Machine and Associated Robotic Experimentations}, booktitle = {Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life}, year = {1992}, editor = {F.J. Varela and P. Bourgine}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, pages = {310-314}, abstract = {ABSTRACT Describes a possible application of computing techniques inspired by natural life mechanisms to an artificial life creature, namely a small mobile robot, called KitBorg. Probabilistic inference suggests that any cognitive problem may be split in two optimization problems. The first one called the dynamic inference problem is an abstraction of learning, the second one, namely, the static inference problem, being a mathematical metaphor of pattern association. Other optimization technics should be considered in that context and especially genetic algorithms. The purpose of this paper is to describe the state of the art of the investigations which the Author is "akin" about that question using a parallel genetic algorithm. The author first "ecall" the principles of probabilistic inference, then he presents briefly the parallel genetic algorithm and the ways it is used to deal with both optimization problems, to finally conclude about ongoing robotic experimentations and future planned extensions.}, network = {boltzmann}, encoding = { }, evolves = { }, applications = {robot controller} } @inproceedings{Bishop93, key = {genetic algorithms connectionism neural networks cogann application paint industry}, author = {J.M. Bishop and M.J. Bushnell and A. Usher and S. Westland}, title = {Genetic Optimization of Neural Network Architectures for Colour Recipe Prediction}, booktitle = {Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms}, year = {1993}, pages = {719-725}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = {optimization} } @article{Bornholdt92, key = {connectionism, cogann ref}, author = {Stephan Bornholdt and Dirk Graudenz}, title = {General Assymetric Neural Networks and Structure Design by Genetic Algorithms}, journal = {Neural Networks}, year = {1992}, volume = {5}, number = {2}, pages = {327-334}, note = {DESSY 91-046, Deutsches Electronen-Synchrotron, Hamburg, Germany, MAY 1991}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @techreport{Bornholdt93, key = {connectionism, cogann}, author = {Stephan Bornholdt and Dirk Graudenz}, title = {General Assymetric Neural Networks and Structure Design by Genetic Algorithms: A Learning Rule for Temporal Patterns}, institution = {Lawrence Berkeley Laboratory, University of California}, year = {1993}, month = {July}, address = {Berkeley, CA}, type = {HD-THEP-93-26 LBL-34384}, publisher = {Lawrence Berkeley Laboratory, University of California}, abstract = {ABSTRACT A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.}, topology = {general, recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = {temporal pattern recognition} } @article{Brassinne93, key = {genetic algorithms connectionism neural networks cogann}, author = {P. de la Brassinne}, title = {Genetic Algorithms and Learning of Neural Networks}, journal = {Bulletin Scientifique de l'Association des Ingenieurs Electriciens sortis de l'Institut Electrotechnique Montefiore}, year = {1993}, volume = {106}, number = {1}, pages = {41-,58}, abstract = {ABSTRACT The Author sought to apply genetic algorithms to two concrete industrial problems which caused trouble to classical optimization techniques (they were usually trapped into local minima), without positive results. One of the reasons was that the solutions among the population were too close to one another too early in the search process. Another was the unsuitability of the operators employed to create new solutions for the neural network optimization problem. Attempts at application to control problems, where backpropagation could not be used, yielded disappointing results except for very simple problems such as the inverted pendulum. An explanation of these findings is suggested.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {optimization} } @inproceedings{Braun93, key = {genetic algorithms connectionism neural networks cogann}, author = {H. Braun and J. Weisbrod}, title = {Evolving Neural Feedforward Networks}, booktitle = {Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C}, publisher = {Springer-Verlag}, pages = {25-32}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @article{Brill92, key = {genetic algorithms, connectionism, cogann ref}, author = {F.Z. Brill and D.E. Brown and W.N. Martin}, title = {Fast Genetic Selection of Features for Neural Network Classifiers}, journal = {IEEE Transactions on Neural Networks}, year = {1992}, month = {March}, volume = {3}, number = {2}, pages = {324-328}, abstract = {ABSTRACT - The task of classifiers is to determine the appropriate class name when presented with a sample from one of several classes. In forming the sample to present to the classifier, there may be a large number of measurements one can make. Feature selection addresses the problem of determining which of these measurements are the most useful for determining the pattern's class. In this paper, we describe experiments using a genetic algorithm for feature selection in the context of neural network classifiers, specifically, counterpropagation networks. We present two novel techniques in our application of genetic algorithms. First, we configure our genetic algorithm to use an approximate evaluation in order to reduce significantly the computation required. In particular, though our desired classifiers are counterpropagation networks, we use a nearest-neighbor classifier to evaluate feature sets. We show that the features selected by this method are effective in the context of counterpropagation networks. Second, we propose a method we call training set sampling, in which only a portion of the training set is used on any given evaluation. Again, significant computational savings can be made by using this method, i.e., evaluations can be made over an order of magnitude faster. This method selects feature sets that are as good as and occasionally better for counterpropagation than those chosen by an evaluation that uses the entire training set.}, topology = {counterpropagation}, network = { }, encoding = { }, evolves = { }, applications = {pattern classification} } @article{Bukatova92, key = {genetic algorithms connectionism neural networks cogann}, author = {I. L. Bukatova}, title = {Evolutionary Computer}, journal = {Proceedings of the RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers.}, year = {1992}, month = {October 7-10}, volume = {I}, pages = {467-477}, address = {Rostov-on-Don, Russia}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Calvin87, author = {Calvin, W.H.}, title = {The Brain as a Darwin Machine}, journal = {Nature}, year = {1988}, volume = {330}, pages = {33-43}, network = { }, encoding = { }, applications = { } } @techreport{Carugo91, key = {connectionism, backpropagation, cogann ref}, author = {Marcelo H. Carugo}, title = {Optimization of Parameters of a Neural Network, applied to Document Recognition, using Genetic Algorithms}, institution = {N.V. Philips}, year = {1991}, address = {Eindhoven, The Netherlands}, type = {Nat. Lab. Technical Note No. 049/91}, publisher = {N.V. Philips}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {parameters}, applications = {image recognition} } @inproceedings{Caudell90, key = {genetic algorithms, neural networks, connectionism, constrained weights, implementation of neural networks, electro-optical systems, rms error minimization, convoluted error surfaces, problem: parity, parametric connectivity, cogann ref}, author = {Thomas P. Caudell}, title = {Parametric Connectivity: Feasibility of Learning in Constrained Weight Space}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1990}, pages = {667-675}, volume = {I}, abstract = {Uses constrained (linked) weights (ie, spread networks) trained via genetic search.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {parity} } @inproceedings{Caudell89, key = {neural networks, connectionism, constrained weights, implementation of neural networks, electro-optical systems, rms error minimization, convoluted error surfaces, problem: parity, parametric connectivity, cogann ref}, author = {Thomas P. Caudell and Charles P. Dolan}, title = {Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms}, booktitle = {Proceedings of the Third International Conference on Genetic Algorithms}, year = {1989}, pages = {370-374}, abstract = {Uses constrained (linked) weights (ie, spread networks) trained via genetic search.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {parity} } @article{Caudill91, key = {genetic algorithm connectionism, cogann ref}, author = {Maureen Caudill}, title = {Evolutionary Neural Networks}, journal = {AI Expert}, year = {1991}, month = {March}, pages = {28-33}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Chalmers90, key = {cogann ref}, author = {David J. Chalmers}, title = {The Evolution of Learning: An Experiment in Genetic Connectionism}, booktitle = {Proceedings of the 1990 Connectionist Summer School}, year = {1990}, editor = {D.S. Touretsky and J.L. Elman and T.J Sejnowski and G.E. Hinton}, publisher = {Morgan Kaufmann}, pages = {81-90}, topology = { }, network = { }, encoding = { }, evolves = {learning rule}, applications = { } } @inproceedings{Chang91, key = {connectionism}, author = {E. Chang and R. Lippmann}, title = {Using Genetic Algorithms to Improve Pattern Classification Performance}, booktitle = {Neural Information Processing Systems -- NIPS 3}, year = {1991}, publisher = {Morgan Kaufmann}, pages = {797-803}, editors = {David Touretzky}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {pattern classification} } @inproceedings{Chen92, key = {genetic algorithms, connectionism}, author = {Qi Chen and W. A. Weigand}, title = {Neural Net Model of Batch Processes and Optimization Based on an Extended Genetic Algorithm}, booktitle = {Proceedings of the International Joint Conferenc on Neural Networks}, year = {1992}, pages = {IV-519 - IV-524}, abstract = {ABSTRACT This paper investigates the use of neural network for modeling the batch processes. The consideration of the dynamics of batch processes, a cascade neural network which is the combination of BPN and Euler's numerical integration method, is successfully used to model of batch processes. In terms of this neural network model, an extended genetic algorithm is adopted to generate the optimal trajectory for improving the desired process performance. The genetic algorithm is a general methodology for searching a solution space in a manner analogous to the natural selection procedure in biological evolution. With the motivation of modern genetic techonology, the rule-inducer genetic algorithm is proposed for dynamic optimization of batch processes. The simulation study of a typical biochemical process shows this neural network modeling technique has a good generalization of the batch process and the extended real-value genetic algorithm has a good capability to solve the complicated dynamical optimization problems.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {optimization} } @inproceedings{Chu93, key = {Connectionism, Genetic Algorithms, cogann}, author = {C. H. Chu and C. R. Chow}, title = {A Genetic Algorithm Approach to Supervised Learning for Multilayered Networks}, booktitle = {World Congress on Neural Networks}, year = {1993}, pages = {IV744 - IV747}, abstract = {ABSTRACT A neural network learning algorithm based on genetic algorithms (GAs) for multilayered networks is described. The present method does not require that the input-output pairs for each layer to be known "a priori", since all modules are trained concurrently. For an N-module system, N separate pools of chromosomes are maintained and updated. The algorithm is tested using the 4-bit parity problem and a classification problem. Experiment results are presented and discussed.}, topology = {feed-forward}, network = { }, encoding = { }, evolves = { }, applications = {4-parity, classification} } @inproceedings{Collins90, key = {cogann ref, genetic algorithms, connectionism}, author = {R. Collins and D. Jefferson}, title = {An Artificial Neural Network Representation for Artificial Organisms}, booktitle = {Proceedings of the Conference on Parallel Problem Solving from Nature}, year = {1990}, pages = {259-263}, topology = {general}, network = { }, encoding = {direct}, evolves = {parameters}, applications = {simulated world} } @incollection{Compiani89, author = {Compiani, M. and Montanari D. and Serra R. and Valastro G.}, title = {Classifier Systems and Neural Networks}, booktitle = {Parallel Architectures and Neural Networks}, year = {1989}, editor = {Caianiello E.R.}, publisher = {World Scientific Press, Singapore}, pages = {33-43}, network = { }, encoding = {indirect, classifier systems}, applications = { } } @inproceedings{Das92, key = {genetic algorithms, connectionism, neural networks}, author = {Rajarshi Das and Darrell Whitley}, title = {Genetic Sparse Distributed Memories.}, booktitle = {Proceedings of the Conference on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, pages = {97-107}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Dasgupta92, key = {genetic algorithms, connectionism, neural networks}, author = {Dipankar Dasgupta and Douglas McGregor}, title = {Designing Application-Specific Neural Networks using the Structured Genetic Algorithm.}, booktitle = {Proceedings of the International Conference on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, pages = {87-96}, topology = {feed-forward}, network = { }, encoding = {direct}, evolves = {parameters}, applications = {xor, 4-2-4 encoder-decoder} } @inproceedings{Davis88, key = {connectionism, neural networks, formal equivalence, classifier systems, mapping networks to classifiers genetic algorithms}, author = {Lawrence Davis}, title = {Mapping Classifier Systems into Neural Networks}, booktitle = {Proceedings of the Workshop on Neural Information Processing Systems 1}, year = {1988}, pages = {49-56}, topology = { }, network = { }, encoding = {indirect, classifier systems}, evolves = { }, applications = { } } @conference{Davis89, key = {novel operators, adaptive parameter optimization, neural networks, connectionism, cogann ref}, author = {Lawrence Davis}, title = {Adapting Operator Probabilities in Genetic Algorithms}, booktitle = {Proceedings of the Third International Conference on Genetic Algorithms}, year = {1989}, pages = {61-69}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Davis89a, author = {Lawrence Davis}, title = {Mapping Neural Networks into Classifier Systems}, booktitle = {Proceedings of the 3rd International Conference on Genetic Algorithms}, year = {1989}, pages = {375-378}, network = { }, applications = { } } @article{DeRouin92, key = {genetic algorithms connectionism neural networks cogann}, author = {E. DeRouin and J. Brown}, title = {Alternative Learning Methods for Training Neural Network Classifiers}, journal = {Proceedings of the SPIE - The International Society for Optical Engineering}, year = {1992}, volume = {1710, pt.1}, pages = {II-474-II-483}, abstract = {ABSTRACT Neural networks have proven very useful in the field of pattern classification by mapping input patterns into one of several categories. Rather than being specifically programmed, backpropagation networks (BPNs) 'learn' this mapping by exposure to a training set, a collection of input pattern samples matched with their corresponding output classification. The proper construction of this training set is crucial to successful training of a BPN. One of the criteria to be met for proper construction of a training set is that each of the classes must be adequately represented. A class that is represented less often in the training data may not be learned as completely or correctly, impairing the network's discrimination ability. The degree of impairment is a function of (among other factors) the relative number of samples of each class used for training. The paper addresses the problem of unequal representation in training sets by proposing two alternative methods of learning. One adjusts the learning rate for each class to achieve user-specified goals. The other utilizes a genetic algorithm to set the connection weights with a fitness function based on these same goals. These methods are tested using both artificial and real-world training data.}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {parameters}, applications = {classification} } @article{Dessert92, key = {genetic algorithms connectionism neural networks cogann}, author = {P.E. Dessert}, title = {Anomaly Detection in Data Using Neural Networks With natural selection}, journal = {Proceedings of the SPIE - The International Society for Optical Engineering}, year = {1992}, volume = {1710, pt.1}, pages = {II-725--II-733}, abstract = {ABSTRACT Frequently, time series data taken off machines contains erroneous data points due to errors in the measurement of the data. One such instance of measuring devices recording anomalies occurs in the crash testing of vehicles. Force and acceleration data is collected which an engineer inspects for anomalies, correcting those that are found. Artificial Neural Network (ANN) technology was successfully applied to this problem to eliminate the cost and delay of this manual process. The Author employed " machine learning algorithm that simulates the Darwinian concept of survival of the fittest known as the Genetic Learning Algorithm (GLA). By combining the strength of the GLA and ANNs, a network architecture was created that optimized the size, speed, and accuracy of the ANN. This hybridized system also used the GLA to determine the smallest number of inputs into the ANN that were necessary to detect anomalies in data. This algorithm is known as GENENET, and is described in the paper.}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @incollection{Dodd91, key = {genetic algorithms, connectionism, dolphin vocalization}, author = {N. Dodd}, title = {Optimization of Network Structure using Genetic Techniques}, booktitle = {Applications of Artificial Intelligence in Engineering VI}, year = {1991}, editor = {G. Rzevski and R. A. Adey}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Dodd91a, key = {connectionism}, author = {N. Dodd and D. Macfarlane and C. Marland}, title = {Optimization of Artificial Neural Network Structure Using Genetic Techniques Implemented on Multiple Transputers}, booktitle = {Proceedings of Transputing '91}, year = {1991}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Dolan87, key = {genetic algorithm, connectionism, evolve neural net architecture competitive learning, Hebbian learning, CRAM, cogann ref}, author = {Charles P. Dolan and Michael G. Dyer}, title = {Toward the Evolution of Symbols}, booktitle = {Proceedings of the Second International Conference on Genetic Algorithms}, year = {1987}, editor = {John J. Grefenstette}, publisher = {Lawrence Erlbaum Associates}, address = {Hillsdale, New Jersey}, pages = {123-131}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Dolan87a, key = {genetic algorithm, connectionism, evolve neural net architecture competitive learning, Hebbian learning, CRAM, cogann ref}, author = {Charles P. Dolan and Michael G. Dyer}, title = {Symbolic Schemata in Connectionist Memories: Role Binding and the Evolution of Structure}, institution = {AI Laboratory, University of California, Los Angeles}, year = {1987}, type = {Technical Report UCLA-AI-87-11}, publisher = {UCLA AI Laboratory}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Dominic92, key = {genetic algorithms, connectionism, hill-climbing, mutation only, cogann ref}, author = {S. Dominic and R. Das and D. Whitley and C. Anderson}, title = {Genetic Reinforcement Learning for Neural Networks}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1992}, pages = {II-71 - II-76}, abstract = {Abstract The genetic algorithms which have been shown to yield good performance for neural network weight optimization are really genetic hill-climbers, with a strong reliance on mutation rather than hyperplane sampling. Neural control problems are more appropriate for these genetic hill-climbers than supervised learning applications because in reinforcement learning applications gradient information is not directly available. Genetic reinforcement learning produces competitive results with AHC, another reinforcement learning paradigm for neural networks that employs temporal difference methods. The genetic hill-climbing algorithm appears to be robust over a wide range of learning conditions.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {controller} } @inproceedings{Dress87a, key = {genetic algorithms, connectionism, cogann ref}, author = {W. B. Dress}, title = {Darwinian Optimization of Synthetic Neural Systems}, booktitle = {Proceeding of the IEEE First Annual International Conference on Neural Networks}, year = {1987}, topology = { }, network = { }, encoding = { }, evolves = {connectivity?}, applications = { } } @conference{Dress89, key = {connectionism, cogann ref}, author = {W. B. Dress}, title = {Genetic Optimization in Synthetic Systems}, year = {1989}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @book{Dress90, key = {genetic algorithms, connectionism, cogann ref}, author = {W. B. Dress}, title = {Electronic Life and Synthetic Intelligent Systems}, year = {1990}, publisher = {Instrumentation and Controls Division, Oak Ridge Natuional Laboratory}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Dress90a, key = {genetic algorithms, connectionism, cogann ref}, author = {W. B. Dress}, title = {In-Silico Gene Expression: A Specific Example and Possible Generalizations}, booktitle = {Proceedings of Emergence and Evolution of Life-Forms}, year = {1990}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Dress87, key = {connectionism, genetic algorithms, cogann ref}, author = {W. B. Dress and J. R. Knisley}, title = {A Darwinian Approach to Artificial Neural Systems}, booktitle = {1987 IEEE Conference on Systems, Man, and Cybernetics}, year = {1987}, month = {Oct}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Eberhart91, key = {connectionism, cogann ref}, author = {R. C. Eberhart and R. W. Dobbins}, title = {Designing Neural Network Explanation Facilities Using Genetic Algorithms}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1991}, pages = {1758-1763}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Eberhart92, key = {genetic algorithms, connectionism, neural networks}, author = {Russell C. Eberhart}, title = {The Role of Genetic Algorithms in Neural Network Query-Based Learning and Explanation Facilities}, booktitle = {Proceedings of the Conference on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, pages = {169-183}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @book{Edelman87, author = {Edelman G.M.}, title = {Neural Darwinism: The Theory of Neuronal Group Selection}, year = {1987}, publisher = {Basic Books, New York}, network = { }, applications = { } } @article{Elias92a, key = {genetic algorithms connectionism neural networks cogann}, author = {J.G. Elias}, title = {Target tracking using impulsive analog circuits}, journal = {Proceedings of the SPIE - The International Society for Optical Engineering}, year = {1992}, volume = {1709, pt.1}, pages = {338-350}, abstract = {ABSTRACT The electronic architecture and silicon implementation of an artificial neuron which can be used to process and classify dynamic signals is described. The electrical circuit architecture is modeled after complex neurons in the vertebrate brain which have spatially extensive dendritic tree structures that support large numbers of synapses. The circuit is primarily analog and, as in the biological model system, is virtually immune to process variations and other factors which often plague more conventional circuits. The nonlinear circuit is sensitive to both temporal and spatial signal characteristics but does not make use of the conventional neural network concept of weights, and as such does not use multipliers, adders, look-up-tables, microprocessors or other complex computational devices. The Author shows "ha" artificial neural networks with passive dendritic tree structures can be trained, using a specialized genetic algorithm, to produce control signals useful for target tracking and other dynamic signal processing applications.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {target tracking, signal processing} } @inproceedings{Elias92, key = {genetic algorithms, connectionism, neural networks}, author = {John G. Elias}, title = {Genetic Generation of Connection Patterns for a Dynamic Artificial Neural Network}, booktitle = {Proceedings of the Conference on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, pages = {38-54}, topology = { }, network = { }, encoding = { }, evolves = {connectivity, parameters}, applications = { } } @inproceedings{Falcon91, author = {Falcon, J.F.}, title = {Simulated Evolution of Modular Networks}, booktitle = {Artificial Neural Networks, IWANN91, Granada}, year = {1991}, editor = {Prieto, A.}, publisher = {Lecture notes in Computer Science 540, Springer Verlag}, pages = {204-211}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Farmer86, key = {immune networks, machine learning}, author = {Farmer, D. J. and Packard, N. H. and Perelson, A. S.}, title = {The immune system, adaptation, and machine learning}, journal = {Physica}, year = {1986}, volume = {22D}, pages = {187-204}, topology = { }, network = { }, encoding = { }, evolves = {feature detectors}, applications = {pattern classification} } @inproceedings{Fekadu93, author = {Fekadu, A.A. and Hines, E.L. and Gardner, J.W.}, title = {Genetic Algorithm Design of Neural Net Based Electronic Nose}, booktitle = {Artificial Neural Networks and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {691-698}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Feldman93, key = {connectionism cogann}, author = {David S. Feldman}, title = {Fuzzy Network Synthesis and Genetic Algorithms}, booktitle = {Proceedings of the Fifth International Conference on Genetic Algorithms}, year = {1993}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Fenanzo86, key = {genetic algorithms, connectionism, cogann ref}, author = {Fenanzo~Jr, A. J}, title = {Darwinian Evolution as a Paradigm for AI Research}, journal = {SIGART Newsletter}, year = {1986}, month = {July}, number = {97}, pages = {22-23}, publisher = {Harding Lawson Associates}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{Fielder93, key = {genetic algorithms connectionism neural networks cogann}, author = {D. Fielder and C.O. Alford}, title = {Counting and Naming Connection Islands on a Grid of Conductors}, booktitle = {Proceedings of the Conference on Artificial Neural Networks and Genetic Algorithms}, organization = {ANNGA93}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {731}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @mastersthesis{Floreano92, author = {Floreano, D.}, title = {Patterns of Interactions in Ecosystems of Neural Networks}, year = {1992}, school = {Neural Computation, Dept of Comp Sci., Univ of Stirling}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Floreano93, author = {Floreano, D.}, title = {ROBOGEN: a Software Package for Evolutionary Control Systems}, institution = {Cognitive technology laboratory, Trieste}, year = {1993}, number = {93-01}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {control systems robot} } @unpublished{Floreano91, author = {Floreano, D. and Miglino, O. and Parisi, D.}, title = {Emerging Complex Behaviours in Ecosystems of Neural Networks}, year = {1991}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Floreano94, author = {Floreano, D. and Mondada, F.}, title = {Automatic Creation of an Autonomous Agent: Genetic Evolution of a Neural-Network Driven Robot}, booktitle = {Proceedings of the Conference on Simulation of Adaptive Behavior}, year = {1994}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {robot controller} } @article{Fogel90, author = {Fogel, D.B. and Fogel, L.J. and Porto, V.W.}, title = {Evolving Neural Networks}, journal = {Biological Cybernetics}, year = {1990}, volume = {63}, pages = {487-493}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Fogel93a, author = {Fogel, D.B. and Simpson, P.K.}, title = {Evolving Fuzzy Clusters}, booktitle = {Proceedings of the International Conference on Neural Networks}, organization = {ICNN93}, year = {1993}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Fogel93, author = {Fogel, David B.}, title = {Using Evolutionary Programming to Create Neural Networks that are Capable of Playing Tic-Tac-Toe}, booktitle = {Proceedings of the American Power Conference}, year = {1993}, publisher = {IEEE}, pages = {875-879}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {tic-tac-toe} } @inproceedings{Fogel93b, author = {David B. Fogel}, title = {Using Evolutionary Programming to Create Neural Networks that are Capable of Playing Tic-Tac-Toe}, booktitle = {Proceedings of the International Conference on Neural Networks}, year = {1993}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {tic-tac-toe} } @techreport{Fogel93c, key = {genetic algorithms, connectionism, COGANN}, author = {David B. Fogel and Lawrence J. Fogel}, title = {Method and Apparatus for Training a Neural Network Using Evolutionary Programming}, institution = {United States}, year = {1993}, month = {25 MAY}, type = {Patent 5214746}, pages = {731}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @article{Fontanari91, key = {genetic algorithm connectionism}, author = {J.F. Fontanari and R. Meir}, title = {Evolving a Learning Algorithm for the Binary Perceptron}, journal = {Network}, year = {1991}, volume = {2}, pages = {353-359}, network = {perceptron}, encoding = { }, evolves = {learning rule}, applications = { } } @inproceedings{Freisleben93, author = {Freisleben, B. and H\H{a}rtfelder}, title = {Optimization of Genetic Algorithms by Genetic Algorithms}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {392-399}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Fritzke93, author = {Bernd Fritzke}, title = {Growing Cell Structures -- A Self-Organizing Network for Unsupervised and Supervised Learning}, institution = {International Computer Science Institute}, year = {1993}, month = {may}, address = {1947 Center Street, Suit 600, Berkeley, California 94704}, number = {TR-93-026}, topology = {feed-forward}, network = {self-organizing}, encoding = { }, evolves = {feature detectors}, applications = { } } @inproceedings{Fullmer92, key = {genetic algorithms connectionism neural networks cogann}, author = {B. Fullmer and R. Miikkulainen}, title = {Using Marker-Based Genetic Encoding of Neural Networks to Evolve Finite-State Behaviour}, booktitle = {Toward a Practice of Autonomous Systems. Proceedings of the First European Conference on Artificial Life}, year = {1992}, editor = {F.J. Varela and P. Bourgine}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, abstract = {ABSTRACT A new mechanism for genetic encoding of neural networks is proposed, which is loosely based on the marker structure of biological DNA. The mechanism allows all aspects of the network structure, including the number of nodes and their connectivity, to be evolved through genetic algorithms. The effectiveness of the encoding scheme is demonstrated in an object recognition task that requires artificial creatures (whose behavior is driven by a neural network) to develop high-level finite-state exploration and discrimination strategies. The task requires solving the sensory-motor grounding problem, i.e., developing a functional understanding of the effects that a creature's movement has on its sensory input.}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = {object recognition} } @inproceedings{Gallagher92, author = {Gallagher, J. C. and Beer, R. D}, title = {A Qualitative Dynamical Analysis of Evolved Locomotion Control}, booktitle = {From Animals to Animats, Proceedings of the Second International Conference on Simuation of Adaptive Behaviour (SAB 92)}, year = {1992}, editor = {Roitblat, H. and Meyer, J-A. and Wilson, S.}, publisher = {The MIT Press, Cambridge, MA}, topology = {recurrent?}, network = { }, encoding = { }, evolves = { }, applications = {robot controller} } @inproceedings{Game93, author = {Game, G. W. and James, C. D.}, title = {The Application of Genetic Algorithms to the Optimal Selection of Parameter Values in Neural Networks for Attitude Control Systems}, booktitle = {IEE Colloquium on 'High Accuracy Platform Control in Space'}, year = {1993}, publisher = {IEE, London}, pages = {3/1-3/3}, volume = {Digest No. 1993/148}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @techreport{deGaris89, key = {genetic algorithms, connectionism, cogann ref}, author = {Hugo de Garis}, title = {WALKER, A Genetically Programmed, Time Dependent, Neural Net Which Teaches a Pair of Sticks to Walk}, institution = {Center for AI, George Mason Univ, Virginia}, year = {1989}, type = {Technical Report}, publisher = {Center for AI, George Mason Univ, Virginia}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {controller} } @book{deGaris90, key = {connectionism, cogann ref}, author = {Hugo de Garis}, title = {Genetic Programming: Building Nanobrains with Genetically Programmed Neural Network Modules.}, year = {1990}, publisher = {CADEPS AI Research Unit, Universitye Libre de Bruxelles, CP 194/7, B-1050 Brussels, Belgium}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{deGaris90a, key = {genetic algorithm GenNets connectionism, cogann ref}, author = {Hugo de Garis}, title = {BRAIN Building with GenNets}, journal = {Proceedings of INNC-90}, year = {1990}, volume = {2}, pages = {1036-1039}, address = {Paris}, publisher = {Kluwer Academic Publishers}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{deGaris90b, key = {genetic algorithm GenNets connectionism robot control LIZZY, cogann ref}, author = {Hugo de Garis}, title = {Genetic Programming: Evolution of a Time Dependent Neural Network Module which Teaches a Pair of Stick Legs to Walk}, booktitle = {Proceedings of the 9th European Conference on Artificial Intelligence}, year = {1990}, month = {AUG 6-10}, address = {Stockholm, Sweden}, pages = {204-206}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {controller} } @conference{deGaris92, key = {genetic algorithms, connectionism}, author = {Hugo de Garis}, title = {Exploring GenNet Behaviors Using Genetic Programming to Explore Qualitatively New Behaviors in Recurrent Neural Networks}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, organization = {IJCNN-92}, year = {1992}, pages = {III-547 - III-552}, abstract = {ABSTRACT The neural network research community's preoccupation with convergent networks (until the recent rise of 'recurrent backpropagation' algorithms [e.g. WILLIAMS \& ZIPSER 1989ab]) has not been unreasonable. Relatively little analytical work had been done on neural networks whose inputs and/or outputs are time-dependent, hence few guidelines existed on how to train such networks. Consequently, research concentrated on more restrictive 'static' neural nets such as 'feedforward' (Backprop) [RUMELHART \& McCLELLAND 1986] and "Hopfield" (clamped inputs, convergent outputs) [HOPFIELD 1982]. This emphasis on convergence was unfortunate, because the true richness of neural network dynamics is to be found when inputs and/or outputs are time-dependent. This paper shows that Genetic Programming techniques (i.e. using Genetic Algorithms to build/evolve complex systems) can be applied successfully to training nonconvergent networks, and presents some examples of their extraordinary behavioral versatility. This paper terminates by comparing GenNet behaviors with those generated by the new 'recurrent backpropagation' algorithms [WILLIAMS \& ZIPSER 1989ab]. It is claimed that the GenNet behaviors are a lot more flexible and interesting because they do not require the training process to be "closely supervised".}, topology = {recurrent}, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{deGaris92a, key = {genetic algorithms connectionism neural networks cogann}, author = {Hugo de Garis}, title = {Steerable GenNets: the Genetic Programming of Steerable Behaviors in GenNets}, booktitle = {Toward a Practice of Autonomous Systems. Proceedings of the First European Conference on Artificial Life}, year = {1992}, editor = {F.J. Varela and P. Bourgine}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, abstract = {ABSTRACT Shows how genetic programming techniques (i.e. the art of applied evolution, or building complex systems using the genetic algorithm) can be used to evolve dynamic behaviors in neural systems which are controllable or steerable. The genetic algorithm evolves the weights of a fully-connnected time-dependent neural network (called a GenNet), such that the same GenNet is capable of generating two separate time-dependent behaviors, depending upon the setting of two different values of a clamped input control variable. By freezing these weights in the GenNet and then applying intermediate control values, one obtains intermediate behaviors, showing that the GenNet has generalized its behavioral learning. It has become controllable or steerable. This principle is applicable to the evolution of many controllable neural behaviors and is useful in the construction of artificial creatures (with artificial nervous systems) based on neural modules. One simply evolves two behaviors at different settings of the control input so that the GenNet generalizes its behavioral learning. In this paper, a concrete example of this process is given in the form of the genetic programming of a variable frequency generator GenNet. This paper ends with a discussion on the handcrafters vs. evolutionists controversy, concerning future approaches to artificial creature (biot) building.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{deGaris93, key = {genetic algorithms connectionism neural networks cogann}, author = {Hugo de Garis}, title = {Circuits of Production Rule GenNets. The Genetic Programming of Artificial Nervous Systems}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, organization = {ANNGA93}, year = {1993}, pages = {699-705}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{deGaris93a, key = {connectionism, genetic algorithms, cogann, Genetic Programming, GenNets Genetically Programmed Neural Network Modules, Artificial Nervous Systems, Biots Biological Robots, Darwinian Robotics, 1000-GenNet Biots, GenNet Accelerators, GenNet Shaping.}, author = {Hugo de Garis}, title = {Incremental Evolution of Neural Networks: Genetic Programming in Incremental Steps}, booktitle = {Proceedings of the World Congress on Neural Networks}, organization = {WCNN93}, year = {1993}, pages = {II447 - II450}, abstract = {ABSTRACT This paper addresses itself to the question of Incremental Evolution of neural networks, which is defined to be the art of evolving neural networks in incremental steps, using Genetic Algorithms. One evolves the weight values of a fully connected neural network (called a GenNet [de Garis 1990, 1993]) containing N neurons to perform T tasks, and then takes the result (i.e. the evolved weights of the N neurons) and adds a few more neurons dN, to evolve the performance of a few more tasks dT. This paper investigates (a) whether this can be done at all, (b) whether is is faster to evolve an N + dN GenNet performing T + dT tasks from scratch or to do it incrementally (1.e. [N,T] then [N+dN,T+dT]), and (c) how the two approaches (i.e. from scratch or incremental) compare in task performance quality. Incremental Evolution will become an important issue when the various brain builder groups around the world (i.e.groups using evolved neural network modules to build artificial nervous systems for biological robots (biots), e.g. Beer's group at Case Western Reserve University USA, Cliff et al's group at Sussex University UK, and the Author's group "" ATR Japan [de Garis 1993] are confronted with the decision whether to start from scratch when desiring to evolve biots with a greater number of behaviors, or to increment their already evolved nervous systems. Nature obviously had to increment.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Gierer88, key = {genetic algorithms, connectionism, cogann ref}, author = {A. Gierer}, title = {Spatial Organization and Genetic Information in Brain Development}, journal = {Biological Cybernetics}, year = {1988}, volume = {59}, pages = {13-21}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{Gonzalez-Seco92, key = {genetic algorithms, connectionism, GLANN}, author = {Jose Gonzalez-Seco}, title = {A Genetic Algorithm as the Learning Procedure for Neural Networks}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, organization = {IJCNN-92}, year = {1992}, pages = {I-835 - I-840}, abstract = {ABSTRACT The relationship between genetic algorithms and neural networks has been somewhat one directional. In most cases a genetic algorithm has been used to generate better neural networks. In this paper we combine the use of genetics algorithms and neural networks, but from a conceptually different point of view. We show that it is possible to use a genetics algorithm as the learning algorithm for a neural network. In our model the neural network has a fixed architecture and processes binary strings using genetic operators.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @techreport{Gruau92, author = {Frederic Gruau}, title = {Cellular Encoding of Genetic Neural Network}, institution = {Laboratoire de l'Informatique du Parall\'elisme, Ecole Normale Sup\'erieure de Lyon}, year = {1992}, type = {Research Report 92.21}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @inproceedings{Gruau92a, author = {Frederic Gruau}, title = {Genetic Synthesis of Boolean Neural Networks with a Cell Rewriting Developmental Process}, booktitle = {Proceedings of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, editor = {Whitley, L.D. and Schaffer, J.D.}, publisher = {IEEE Computer Society Press}, pages = {55-74}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @unpublished{Gruau92b, author = {Frederic Gruau}, title = {Cellular Encoding of Genetic Neural Networks I. Theoretical Properties}, year = {1992}, note = {submitted to evolutionnary computation}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Gruau93, author = {Frederic Gruau}, title = {A Learning and Pruning Algorithm for Genetic Neural Networks}, booktitle = {European Symposium on Artificial Neural Networks}, year = {1993}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Gruau93a, key = {genetic algorithms, connectionism cogann}, author = {Frederic Gruau}, title = {Genetic Synthesis of Modular Neural Networks}, booktitle = {Proceedings of the Fifth International Conference on Genetic Algorithms}, year = {1993}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @incollection{Gruau94, author = {Frederic Gruau}, title = {Genetic Micro Programming of Neural Networks}, booktitle = {Advances in Genetic Programming}, year = {1994}, editor = {Kim Kinnear}, publisher = {MIT Press}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @phdthesis{Gruau94a, author = {Frederic Gruau}, title = {Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm}, year = {1994}, school = {PhD Thesis, Ecole Normale Sup\'erieure de Lyon}, note = {anonymous ftp: lip.ens-lyon.fr (140.77.1.11) directory pub/Rapports/PhD file PhD94-01-E.ps.Z (english) PhD94-01-F.ps.Z (french)}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @techreport{Gruau93b, author = {Frederic Gruau and Darrell Whitley}, title = {The Cellular Development of Neural Networks: the Interaction of Learning and Evolution}, institution = {Laboratoire de l'Informatique du Parallelisme, Ecole Normal Superieure de Lyon}, year = {1993}, number = {93-04}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity, parameters}, applications = { } } @article{Gruau93c, author = {Frederic Gruau and Darrell Whitley}, title = {Adding Learning to the Cellular Developmental Process: a Comparative Study}, journal = {Evolutionary Computation}, year = {1993}, volume = {1}, number = {3}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity}, applications = { } } @techreport{Gruau93d, author = {Frederic Gruau and Darrell Whitley}, title = {Adding Learning to the Cellular Developmental Process: a Comparative Study}, institution = {Laboratoire de l'Informatique du Parall\'elisme, Ecole Normale Sup\'erieure de Lyon}, year = {1993}, type = {Research Report RR93-04}, topology = {general}, network = { }, encoding = {indirect cellular encoding}, evolves = {connectivity}, applications = { } } @article{Gruau93e, key = {genetic algorithm connectionism neural networks cogann}, author = {Fredric Gruau}, title = {Cellular Encoding as a Graph Grammar}, journal = {IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives}, year = {1993}, month = {22-23 April}, volume = {(Digest No.092)}, pages = {17/1-10}, publisher = {IEE}, address = {London}, abstract = {ABSTRACT Cellular encoding is a method for encoding a family of neural networks into a set of labeled trees. Such sets of trees can be evolved by the genetic algorithm so as to find a particular set of trees that encodes a family of Boolean neural networks for computing a family of Boolean functions. Cellular encoding is presented as a graph grammar. A method is proposed for translating a cellular encoding into a set of graph grammar rewriting rules of the kind used in the Berlin algebraic approach to graph rewriting. The genetic search of neural networks via cellular encoding appears as a grammatical inference process where the language to parse is implicitly specified, instead of explicitly by positive and negative examples. Experimental results shows that the genetic algorithm can infer grammars that derive neural networks for the parity, symmetry and decoder Boolean function of arbitrary large size.}, topology = {general}, network = { }, encoding = {cellular encoding, graph grammar}, evolves = {connectivity, parameters}, applications = {parity etc} } @techreport{Guha92, key = {connectionism, neural networks, cogann}, author = {Aloke Guha and Steven A. Harp and Tariq Samad}, title = {Genetic Algorithm Synthesis of Neural Networks}, institution = {United States}, year = {1992}, month = {18 AUG}, type = {Patent 5140530}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @conference{Guo92, key = {genetic algorithms, connectionism, neural networks}, author = {Zhichao Guo and Robert Uhrig}, title = {Using Genetic Algorithms to Select Inputs for Neural Networks}, booktitle = {Proceedings of the Workshop on Combinations of Genetic Algorithms and Neural Networks}, organization = {COGANN92}, year = {1992}, pages = {223-234}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Hoffgen91, author = {H\H{o}ffgen, K-U. and Siemon, H.P. and Ultsch, A}, title = {Genetic Improvement of Feedforward Nets for Approximating Functions}, booktitle = {Proceedings of the Conference on Parallel Problem Solving from Nature}, year = {1991}, editor = {Schwefel, H-P. and M\H{a}nner, R.}, publisher = {Lecture notes in Computer Science 496, Springer Verlag}, pages = {302-306}, topology = {feed-forward}, network = { }, encoding = { }, evolves = { }, applications = {function approximation} } @inproceedings{Hancock89, author = {Hancock, P. J. B.}, title = {Optimising Parameters in Neural Net Simulations by Genetic Algorithm}, booktitle = {Mini-Symposium on Neural Network Computation}, year = {1989}, publisher = {Rank Prize Funds, Broadway: unpublished}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @inproceedings{Hancock90, key = {connectionism, cogann ref}, author = {P. J. B. Hancock}, title = {GANNET: Design of a Neural Network for Face Recognition by Genetic Algorithm}, booktitle = {Proceedings of the IEEE Workshop on Genetic Algorithms, Simulated Annealing and Neural Networks}, year = {1990}, address = {University of Glasgow, Scotland}, topology = { }, network = { }, encoding = { }, evolves = {connectivity?}, applications = {face recognition} } @phdthesis{Hancock92, author = {Hancock, P. J. B.}, title = {Coding Strategies for Genetic Algorithms and Neural Nets}, year = {1992}, school = {Department of Computing Science and Mathematics, University of Stirling}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Hancock92a, author = {Hancock, P. J. B.}, title = {Recombination Operators for the Design of Neural Nets by Genetic Algorithms}, booktitle = {Parallel Problem Solving from Nature 2}, year = {1992}, editor = {M\H{a}nner, R. and Manderick, B.}, publisher = {Elsevier, North Holland}, pages = {441-450}, topology = { }, network = { }, encoding = { }, evolves = {parameters, connectivity?}, applications = { } } @inproceedings{Hancock92b, author = {Hancock, P. J. B.}, title = {Pruning Neural Nets by Genetic Algorithm}, booktitle = {Proceedings of the International Conference on Artificial Neural Networks, Brighton}, year = {1992}, editor = {Aleksander, I. and Taylor, J.G.}, publisher = {Elsevier}, pages = {991-994}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Hancock92c, author = {Hancock, P. J. B.}, title = {Genetic Algorithms and Permutation Problems: a Comparison of Recombination Operators for Neural Net Structure Specification}, booktitle = {Proceedings of COGANN workshop, IJCNN, Baltimore}, year = {1992}, editor = {Whitley, D.}, publisher = {IEEE}, topology = { }, network = { }, encoding = { }, evolves = {connectivity, parameters}, applications = { } } @inproceedings{Hancock91, author = {Hancock, P. J. B. and Smith, L. S}, title = {GANNET: Genetic Design of a Neural Net for Face Recognition}, booktitle = {Proceedings of the Conference on Parallel Problem Solving from Nature}, year = {1991}, editor = {Schwefel, H-P. and M\H{a}nner, R.}, publisher = {Lecture notes in Computer Science 496, Springer Verlag}, pages = {292-296}, topology = { }, network = { }, encoding = { }, evolves = {connectivity, parameters}, applications = {face recognition} } @techreport{Harp89, author = {Harp, S. A. and Samad, T. and Guha A.}, title = {The Genetic Synthesis of Neural Networks}, institution = {Honeywell CSDD}, year = {1989}, number = {TR CSDD-89-I4852-2}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @inproceedings{Harp89a, author = {Harp, S. A. and Samad, T. and Guha, A.}, title = {Towards the Genetic Synthesis of Neural Networks}, booktitle = {Proceedings of the Third International Conference on Genetic Algorithms}, year = {1989}, editor = {Schaffer, J.D.}, publisher = {Morgan Kaufmann}, pages = {360-369}, institution = {Honeywell CSDD}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @inproceedings{Harp89b, author = {Harp, S. A. and Samad, T. and Guha, A.}, title = {Designing Application-Specific Neural Networks Using the Genetic Algorithm}, booktitle = {Neural Information Processing Systems 2}, year = {1989}, editor = {Touretzky, D.S.}, institution = {Honeywell CSDD}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @article{Harp92, key = {genetic algorithms connectionism neural networks cogann}, author = {Steven A. Harp and Tariq Samad}, title = {Optimizing Neural Networks with Genetic Algorithms}, journal = {Proceedings of the American Power Conference}, year = {1992}, volume = {54 pt 2}, pages = {1138-1143}, publisher = {Illinois Inst of Technology}, address = {Chicago, IL, USA.}, abstract = {ABSTRACT We describe an approach to application-specific neural network design using genetic algorithms. A genetic algorithm is a robust optimization method particularly well suited for search spaces that are high-dimensional, discontinuous and noisy-features that typify the neural network design problem. Our approach is relevant to virtually all neural network applications: it is network-model independent and it permits optimization for arbitrary, user-defined criteria. We have developed an experimental system, NeuroGENESYS, and have conducted several experiments on small-scale problems. Performance improvements over manual designs have been observed, the interplay between performance criteria and network design aspects has been demonstrated, and general design principles have been uncovered.}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @inproceedings{Harp91a, key = {algorithms, connectionism, Kohonen, clustering, vector quantization, cogann ref}, author = {Steven Alex Harp and Tariq Samad}, title = {Genetic Optimization of Self-Organizing Feature Maps}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1991}, pages = {341-346}, journal = {IJCNN-91}, volume = {I}, network = {self-organizing}, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Harp91, author = {Steven Harp and Tariq Samad}, title = {Genetic Synthesis of Neural Network Architecture}, booktitle = {Handbook of Genetic Algorithms}, year = {1991}, editor = {Davis, L.}, publisher = {Van Nostrand Reinhold}, pages = {202-221}, chapter = {15}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = {2-parity, digit recognition, function approximation} } @techreport{Harvey93, author = {Harvey, I. and Husbands, P. and Cliff, D.}, title = {Genetic Convergence in a Species of Evolved Robot Control Architectures}, institution = {Cognitive Science, university of Sussex}, year = {1993}, number = {CSRP 267}, mnote = {See also ICGA93}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {robot controller} } @conference{Hassoun93, key = {connectionism, genetic algorithms, cogann}, author = {Mohamad H. Hassoun and Jing Song}, title = {Multilayer Perceptron Learning Via Genetic Search for Hidden Layer Activations}, booktitle = {Proceedings of the World Congress on Neural Networks}, organization = {WCNN93}, year = {1993}, pages = {III437 - III444}, abstract = {ABSTRACT A new learning technique is proposed for multilayer neural networks based on genetic search, in hidden target space, and gradient descent learning strategies. Our simulations show that the new algorithm combines the global optimization capabilities of genetic algorithms with the speed of gradient descent local search in order to outperform pure descent-based algorithms such as backpropagation. In addition, we show that genetic search in hidden target space is less complex than that of weight space.}, topology = {multi-layered}, network = {multi-layer perceptron}, encoding = { }, evolves = { }, applications = { } } @inproceedings{Heistermann91, author = {Heistermann, J}, title = {The Application of a Genetic Approach as an Algorithm for Neural Networks}, booktitle = {Parallel Problem Solving from Nature}, year = {1991}, editor = {Schwefel, H-P. and M\H{a}nner, R.}, publisher = {Lecture notes in Computer Science 496, Springer Verlag}, pages = {297-301}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Heistermann89, key = {connectionism, genetic algorithms}, author = {J. Heistermann}, title = {Parallel Algorithms for Learning in Neural Networks with Evolution Strategy}, journal = {Parallel Computing}, year = {1989}, volume = {12}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Heistermann90, key = {connectionism}, author = {J. Heistermann}, title = {Learning in Neural Nets by Genetic Algorithms}, booktitle = {Parallel Processing in Neural Systems and Computers}, year = {1990}, editor = {R. Eckmiller and G. Hartmann and G. Hauske}, publisher = {Elsevier Science Publishers}, pages = {165-168}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Heistermann92, key = {genetic algorithms connectionism neural networks cogann}, author = {J. Heistermann}, title = {A Mixed Genetic Approach to the Optimization of Neural Controllers}, booktitle = {CompEuro 1992 Proceedings. Computer Systems and Software Engineering}, year = {1992}, editor = {P. Dewilde and J. Vandewalle}, publisher = {IEEE Comput. Soc. Press}, address = {Los Alamitos, CA, USA}, pages = {459-464}, abstract = {ABSTRACT The Author discusses "om" of the capabilities of genetic algorithms (GAs). GAs are compared with other standard optimization methods like gradient descent or simulated annealing (SA). It is shown that SA is just a special case of GA. The role of a population in the optimization process is demonstrated by an example. GA was applied as a learning algorithm to neural networks.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {controller} } @article{Hinton87, key = {Neural nets genetic algorithms connectionism, cogann ref}, author = {Geoffrey E. Hinton and Stephen J. Nowlan}, title = {How Learning Can Guide Evolution}, journal = {Complex Systems}, year = {1987}, month = {JUN}, volume = {1}, number = {1}, pages = {495-502}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Hintz90, key = {connectionism, genetic algorithm (?)}, author = {K.J. Hintz and J.J. Spofford}, title = {Evolving a Neural Network}, booktitle = {Proceedings of the 5th IEEE International Symposium on Intelligent Control}, year = {1990}, month = {SEPT}, editor = {A. Meystel}, publisher = {IEEE Computer Society Press}, address = {Los Alamitos, CA}, pages = {479-484}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Ho92, key = {genetic algorithms connectionism neural networks cogann}, author = {A.W. Ho and G.C. Fox}, title = {Competitive-Cooperative System of Distributed Artificial Neural Agents}, booktitle = {Parallel Computing: Problems, Methods and Applications. Selection of Papers Presented at the Conference on Parallel Computing: Achievements, Problems and Prospects}, year = {1992}, editor = {P. Messina and A. Murli}, publisher = {Elsevier, Amsterdam, Netherlands}, pages = {499-507}, abstract = {ABSTRACT A framework for simulations of hierarchical organizations of interacting, distributed artificial agents on distributed-memory, MIMD computers is presented. Interactions among aggregates of intelligent agents in an organization are restricted to obey competition and cooperation criteria. Each intelligent agent in an organization is a parallel implementation of a feedforward multilayer perceptrons neural network using error backpropagation (BP) as the learning rule. In this preliminary study, domination, viewed as a type of deterministic genetic algorithm (GA,) is chosen to be the preferred form of interaction. The framework exploits the hierarchical nature intrinsic in an organizational approach to problem-solving. It takes advantage of parallelism at different levels of granularity, from domain decomposition within the agents to coarse grain team-level interaction. Transputer-based simulation results for a test problem of learning the solution to a parity function of predicate order 10 is discussed.}, topology = {multi-layered}, network = {multi-layer perceptron}, encoding = { }, evolves = { }, applications = { } } @article{Holland92, key = {genetic algorithms connectionism neural networks cogann robotics}, author = {O.E. Holland and M.A. Snaith}, title = {Neural Control of Locomotion in a Quadrupedal Robot}, journal = {IEE Proceedings Part F: Radar and Signal Processing}, year = {1992}, month = {DEC}, volume = {139}, number = {6}, pages = {431-436}, abstract = {ABSTRACT The Authors present "esult" of a first study demonstrating that the apparently complex task of controlling walking in a real quadrupedal robot with highly nonlinear interactions between the control elements can be learned quickly by a crude and simple reinforcement learning algorithm. They can as yet say little that is useful about the contribution of reflexes to learned walking, and nothing about the quality of evolved solutions other than that their discovery by applying genetic algorithms to real robots is likely to take a prohibitively long time. However, they hope that their experiences will point the way to more controlled studies of the applications of reinforcement learning to real-world problems, especially to control problems associated with autonomous mobile robots.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {robot controller} } @article{Honavar89a, key = {connectionism neural networks constructive algorithms inductive learning, local architectures, brain modeling, pattern classification}, author = {Honavar, V. and Uhr, L.}, title = {Brain-Structured Networks That Perceive and Learn}, journal = {Connection Science}, year = {1989}, volume = {1}, pages = {139-159}, topology = {feed-forward, locally connected, structured, multi-layered, regular, modular}, network = { }, encoding = { }, evolves = {feature detectors, connectivity, topology}, applications = {pattern classification, vision, brain modeling} } @inproceedings{Honavar89b, key = {connectionism neural networks constructive algorithms inductive learning, pattern classification}, author = {Honavar, V. and Uhr, L.}, title = {A Network of Neuron-Like Units That Learns by Generation As Well As Reweighting of its Links}, booktitle = {Proceedings of the 1988 Connectionist Models Summer School}, year = {1989}, publisher = {Morgan Kaufmann, Palo Alto}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {feature detectors, topology}, applications = {pattern classification} } @inproceedings{Honavar89c, key = {connectionism neural networks constructive algorithms inductive learning, pattern classification}, author = {Honavar, V. and Uhr, L.}, title = {Generation, Local Receptive Fields, and Global Convergence Improve Perceptual Learning in Connectionist Networks}, booktitle = {Proceedings of the Tenth International Joint Conference on Artificial Intelligence}, year = {1989}, publisher = {Morgan Kaufmann, Palo Alto}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {feature detectors, topology}, applications = {pattern classification} } @article{Honavar93, key = {connectionism neural networks constructive algorithms inductive learning, radial basis functions, pattern classification}, author = {Honavar, V. and Uhr, L.}, title = {Generative Learning Structures and Processes for Generalized Connectionist Networks}, journal = {Information Sciences}, year = {1993}, volume = {70}, pages = {75-108}, topology = {feed-forward}, network = { }, encoding = { }, evolves = {feature detectors, connectivity, topology}, applications = {pattern classification} } @inproceedings{Hoptroff90, author = {Hoptroff, R. G. and Hall, T. J. and Burge, R. E.}, title = {Experiments With a Neural Controller}, booktitle = {1990 International Joint Conference on Neural Networks - IJCNN 90}, year = {1990}, publisher = {IEEE, New York}, pages = {735-740}, volume = {2}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {controller} } @inproceedings{Hsu92, author = {Hsu, L.S. and Wu, Z.B.}, title = {Input Pattern Encoding Through Generalized Adaptive Search}, booktitle = {Proceedings of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, editor = {Whitley, L.D. and Schaffer, J.D.}, publisher = {IEEE Computer Society Press}, pages = {235-247}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Huang92, author = {Huang, R.}, title = {Systems Control With the Genetic Algorithm and the Nearest Neighbour Classification}, journal = {CC-AI}, year = {1992}, volume = {9(2-3)}, pages = {225-236}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {controller} } @techreport{Husbands92, author = {Husbands, P. and Harvey, I. and Cliff, D. T.}, title = {Analysing Recurrent Dynamical Networks Evolved for Robot Control}, institution = {University of Sussex, School of Cognitive and Computing Sciences}, year = {1992}, number = {CSRP265}, topology = {recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = {robot controller} } @inproceedings{Ichikawa90, author = {Ichikawa, Y.}, title = {Evolution of Neural Networks and Application to Motion Control}, booktitle = {Proceedings of the IEEE International Conference on Intelligent Motion Control}, year = {1990}, publisher = {IEEE}, pages = {239-245}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = {controller} } @inproceedings{Jacob93, author = {Jacob, C. and Rehder, J.}, title = {Evolution of Neural Net Architectures by a Hierachical Grammar-Based Genetic System}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {72-79}, topology = { }, network = { }, encoding = {indirect, grammar based}, evolves = {connectivity}, applications = { } } @article{Janson92, key = {genetic algorithms connectionism neural networks cogann}, author = {D.J. Janson and J.F. Frenzel}, title = {Application of Genetic Algorithms to the Training of Higher Order Neural Networks}, journal = {Journal of Systems Engineering}, year = {1992}, volume = {2}, number = {4}, pages = {272-276}, abstract = {ABSTRACT Product unit neural networks are a new form of feedforward learning networks in which several summing units are replaced by units capable of calculating a weighted product of inputs. While such networks can be trained using traditional backpropagation, the solution involves the manipulation of complex-valued expressions. As an alternative, this paper investigates the training of product networks using genetic algorithms. Results are presented on the training of a neural network to calculate the optimum width of transistors in a CMOS switch given desired operating parameters. It is shown how local minima affect the performance of the genetic algorithm, and one method of overcoming this is presented.}, topology = {feed-forward}, network = {product-unit networks}, encoding = { }, evolves = {parameters}, applications = { } } @techreport{Jefferson90, author = {Jefferson, D. and Collins, R. and Cooper, C. and Dyer, M. and Flowers, M. and Korf, R. and Taylor, C. and Wang, A.}, title = {Evolution as a Theme in Artificial Life: the Genesys/Tracker System}, institution = {Computer Science, UCLA}, year = {1990}, number = {UCLA-AI-90-09}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Jones93, author = {Jones, A.J.}, title = {Genetic Algorithms and Their Applications to the Design of Neural Networks}, journal = {Neural Computing and Applications}, year = {1993}, volume = {1}, pages = {32-45}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @article{Jones93a, author = {Jones, A.J. and MacFarlane, D.}, title = {Comparing Networks With Differing Neural-Node Functions Using Transputer-Based Genetic Algorithms}, journal = {Neural Computing and Applications}, year = {1993}, volume = {1}, pages = {256-267}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @book{Kadaba90a, key = {vehicle routing, Connectionism, genetic algorithms, XROUTE, expert system, neural network, cogann ref}, author = {Nagesh Kadaba and Kendall E. Nygard}, title = {Improving the Performance of Genetic Algorithms in Automated Discovery of Parameters}, year = {1990}, month = {JAN 25}, publisher = {Dept. of SC and OR, North Dakota State University}, note = {Draft}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @conference{Kargupta91, key = {genetic algorithms selection crowding; relation AI machine learning connectionist networks genetic algorithms, cogann ref}, author = {Hillol Kargupta and Robert E. Smith}, title = {System Identification with Evolving Polynomial Networks}, booktitle = {Proceedings of the Fourth International Conference on Genetic Algorithms}, year = {1991}, pages = {370-376}, abstract = {Abstract: The construction of models for prediction and control of initially unknown, potentially nonlinear systems is a difficult, fundamental problem in machine learning and engineering control. In this paper, a {\em genetic algorithm} (GA) based technique is used to iteratively form polynomial networks that model the behavior of nonlinear systems. This approach is motivated by the {\em group method of data handling} (GMDH) (Ivakhnenko, 1971), but attempts to overcome the computational overhead and locality associated with the original GMDH. The approach presented here uses a multi-modal GA (Deb, 1989) to select nodes for a network based on an information-theoretic fitness measure. Preliminary results show that the GA is successful in modeling continuous-time and discrete-time chaotic systems. Implications and extensions of this work are discussed.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Karim92, key = {process control, connectionism, genetic}, author = {M.N. Karim and S.L. Rivera}, title = {Use of Recurrent Neural Networks for Bioprocess Identification in On-Line Optimization by Micro-Genetic Algorithms}, journal = {Proceedings of the American Control Conference}, year = {1992}, volume = {3}, pages = {1931-1932}, publisher = {American Automatic Control Council}, address = {Green Valley, AZ,}, abstract = {ABSTRACT The use of recurrent neural networks in bioprocess identification and optimization is investigated. A recurrent neural network is trained on a set of fermentation data, and thereafter used as a nonlinear process model to estimate nonmeasurable process states at different conditions. With the bioprocess state variable information available, an optimization technique can be used to generate optimum controls settings to improve the process performance. This paper explores the use of Micro-Genetic Algorithms as a technique for bioreactor optimization. Simulation results will be discussed based in the fermentative ethanol production by the anaerobic bacteria Zymomonas mobilis.}, topology = {recurrent}, network = { }, encoding = { }, evolves = {parameters}, applications = {optimization} } @inproceedings{Karunanithi92, author = {Karunanithi, N. and Das, R. and Whitley, D.}, title = {Genetic Cascade Learning for Neural Networks}, booktitle = {Proceedings of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, editor = {Whitley, L.D. and Schaffer, J.D.}, publisher = {IEEE Computer Society Press}, pages = {134-145}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @article{Kauffman86, author = {Kauffman, S.A. and Smith, R.G.}, title = {Adaptive Automata Based on Darwinian Selection}, journal = {Physica D}, year = {1986}, volume = {22}, pages = {68-82}, institution = {Univ Pennsylvania}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Keesing91, author = {Keesing, R. and Stork, D.G.}, title = {Evolution and Learning in Neural Networks, the Number and Distribution of Learning Trials Affect the Rate of Evolution}, booktitle = {Advances in Neural Information Processing Systems 3}, year = {1991}, editor = {Lippmann, R.P. and Moody, J.E. and Touretzky, D.S}, publisher = {Morgan Kaufmann}, pages = {804-810}, topology = { }, network = { }, encoding = { }, evolves = {learning rule}, applications = { } } @book{Kerszberg, key = {connectionism, cogann ref}, author = {Michel Kerszberg}, title = {Genetic and Epigenetic Factors in Neural Circuit Wiring (preliminary)}, publisher = {Institut fur Festkorperforschung der}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @incollection{Kerszberg88, key = {genetic algorithms, connectionism, cogann ref}, author = {Michel Kerszberg and Aviv Bergman}, title = {The Evolution of Data Processing Abilities in Competing Automata}, booktitle = {Computer Simulation in Brain Science, Copenhagen, Denmark}, year = {1986}, month = {August}, editor = {Cotterill, R.M.J}, publisher = {Cambridge University Press}, pages = {249-259l}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @phdthesis{Kirby88, key = {genetic algorithms, connectionism, cogann ref}, author = {K.G. Kirby}, title = {Intraneuronal Dynamics and Evolutionary Learning}, year = {1988}, school = {Dept. of Computer Science, Wayne State University}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Kirby86, key = {genetic algorithms, connectionism, cogann ref}, author = {K.G. Kirby and Michael Conrad}, title = {Intraneuronal Dynamics as a Substrate for Evolutionary Learning}, journal = {Physica D}, year = {1986}, volume = {22}, pages = {205-215}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{Kirby89, key = {genetic algorithms, connectionism, cogann ref}, author = {K.G. Kirby and Michael Conrad and R.R. Kampfner}, title = {Evolutionary Learning in Reaction-Diffusion Neurons}, organization = {SUBMITTED TO Bull. Math. Biol.}, year = {1989}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @conference{Kitano90, key = {connectionism, cogann ref}, author = {Hiroaki Kitano}, title = {Empirical Studies on the Speed of Convergence of Neural Network Training Using Genetic Algorithms}, booktitle = {Proceedings of the 8th National Conference on Artificial Intelligence (AAAI-90)}, organization = {PROC AAAI-90}, year = {1990}, publisher = {MIT Press, Cambridge}, pages = {789-795}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @article{Kitano90a, key = {connectionism, cogann ref}, author = {Hiroaki Kitano}, title = {Designing Neural Network Using Genetic Algorithm with Graph Generation System}, journal = {Complex Systems}, year = {1990}, volume = {4}, pages = {461-476}, topology = { }, network = { }, encoding = {graph grammar}, evolves = {connectivity}, applications = { } } @techreport{Kitano92, key = {connectionism, cogann ref}, author = {Hiroaki Kitano}, title = {Neurogenetic Learning: An Integrated Method of Designing and Training Neural Networks using Genetic Algorithms}, institution = {Carnegie Mellon University}, year = {1992}, month = {MAR}, type = {CMU-CMT-92-134}, topology = { }, network = { }, encoding = { }, evolves = {connectivity, parameters}, applications = { } } @article{Kitano93, key = {genetic algorithms connectionism neural networks cogann}, author = {Hiroaki Kitano}, title = {Continuous Generation Genetic Algorithms}, journal = {Journal of the Society of Instrument and Control Engineers}, year = {1993}, month = {Jan.}, volume = {32}, number = {1}, pages = {31-8}, abstract = {ABSTRACT Presents a continuous generation genetic algorithm. Most genetic algorithms use a discrete generation model in which all individuals in a population synchronize mating period. The discrete generation model, however, wastes processor time in parallel implementations when the fitness of each individual (proportionally or reversely) correlates with the computational cost of its evaluation. An example of such a task is neural network design and training. In some cases, over 80been wasted. The continuous generation model mitigates this problem by introducing asynchronous mating, the continuous generation model increases the number of reproduction per a unit-time over 500discrete model. CPU idle time has been minimized to 1/25. Also, a significant improvement in convergence speed has been estimated.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Kouchi92, key = {genetic algorithms connectionism neural networks cogann}, author = {M. Kouchi and H. Inayoshi and T. Hoshino}, title = {Optimization of Neural-Net Structure by Genetic Algorithm with Diploidy and Geographical Isolation Model}, journal = {Journal of Japanese Society for Artificial Intelligence}, year = {1992}, month = {May}, volume = {7}, number = {3}, pages = {509-517}, abstract = {ABSTRACT The structure of a simple neural network is optimized by the use of a genetic-algorithm. The neural network is a perceptron, which has three outputs; the logical AND, OR and XOR of two inputs The evaluation function for optimization is a linear combination of the correctness, the network sizes, and an auxiliary term inducing the optimum solution The chromosome is a vector of the link weights of the network. The genetic operators used are crossing-over and point-mutation on the parent chromosomes Two genetic rules were tested. In the haploidy rule, each individual has single chromosome, and the offspring is generated by crossing-over the parents' chromosomes at a randomly chosen locus and taking one of those crossed-over chromosomes. In the diploidy rule, each individual has a pair of chromosomes, and the offspring's chromosomes are generated by combining the gamete produced through the meiosis of the parents' chromosomes. The other model used in the genetic algorithm is the geographical isolation model, where the entire population is divided into four sub-populations, in which the local selection and reproduction are carried out, though, in some time interval, randomly sampled individuals are exchanged among sub-populations. Comparison was made among four combinations of haploid or diploid, and single-population or multiple sub-populations. Diploidy together with the sub-population model was proved to be the best for this optimization problem. Thus, the optimum structure of network was found.}, network = {perceptron}, encoding = { }, evolves = {connectivity}, applications = { } } @book{Koza92, author = {John R. Koza}, title = {Genetic Programming: On the Programming of Computers by Means of Natural Selection}, year = {1992}, publisher = {MIT Press, Cambridge, Mass.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Koza91, key = {genetic algorithms, connectionism, one-bit adder, cogann ref}, author = {John R. Koza and James P. Rice}, title = {Genetic Generation of Both the Weights and Architecture for a Neural Network}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, organization = {IJCNN-91}, year = {1991}, pages = {397-404}, journal = {IJCNN-91}, volume = {II}, topology = { }, network = { }, encoding = {genetic programming?}, evolves = {connectivity, parameters}, applications = { } } @inproceedings{Krishnakumar92, author = {Krishnakumar, K.}, title = {Immunized Neurocontrol - Concepts and Initial Results}, booktitle = {Proceedings of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, editor = {Whitley, L.D. and Schaffer, J.D.}, publisher = {IEEE Computer Society Press}, pages = {146-168}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Kwiatkowski93a, author = {Kwiatkowski, L. and Stromboni, J.P.}, title = {Neuromimetic Algorithm processing: Tools for Design of Dedicated Architectures}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {706-711}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Lai92, key = {genetic algorithms, connectionism}, author = {W.K. Lai and G.G. Coghill}, title = {Genetic Breeding of Control Parameters for the Hopfield/Tank Neural Net}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, organization = {IJCNN-92}, year = {1992}, pages = {IV-618 - IV-623}, abstract = {ABSTRACT Artificial neural networks, especially the Hopfield/Tank neural net have been used to solve the travelling salesman problem. These networks usually require a set of parameters to be carefully selected and tuned to produce sensible solutions. Genetic Algorithms are basically adaptive systems that transform a population of individuals into new populations, using relatively simple mechanisms. It has the ability to efficiently explore the problem sub-space to produce approximate solutions that are globally competitive. This paper will show how Genetic Algorithms may be used in conjunction with the Hopfield/Tank neural net by breeding an effective set of control parameters in the parameter sub-space to be used by the artificial neural network.}, topology = {hopfield network}, network = { }, encoding = { }, evolves = {parameters}, applications = {travelling salesperson problem} } @mastersthesis{Lange93, author = {Frank Lange}, title = {"Uber den Zusammenhang zwischen Komplexit"at und Generalisierungsf"ahigkeit Neuronaler Netze}, year = {1993}, school = {Universit"at Karlsruhe, Institut f"ur Logik, Komplexit"at und Deduktionssysteme}, note = {Beyond Soft-Weight-Sharing: Soft-Entropy-Minimization}, type = {Diplomarbeit}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Lehar87, author = {Lehar, S. and Weaver, J.}, title = {A Developmental Approach to Neural Network Design}, booktitle = {Proceedings of the IEEE International Conference on Neural Networks}, year = {1987}, publisher = {IEEE Press}, pages = {97-104}, topology = { }, network = { }, encoding = { }, applications = { } } @inproceedings{Lewis92a, key = {genetic algorithms connectionism neural networks cogann programming}, author = {M. Anthony Lewis and Andrew H. Fagg and Alan Solidum}, title = {Genetic Programming Approach to the Construction of a Neural Network for Control of a Walking Robot}, booktitle = {Proceedings of IEEE International Conference on Robotics and Automation}, year = {1992}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, pages = {2618-2623}, volume = {3}, abstract = {ABSTRACT The Authors describe "h" staged evolution of a complex motor pattern generator (MPG) for the control of a walking robot. The experiments were carried out on a six-legged, Brooks-style insect robot. The MPG was composed of a network of neurons with weights determined by genetic algorithm optimization. Staged evolution was used to improve the convergence rate of the algorithm. First, an oscillator for the individual leg movements was evolved. Then, a network of these oscillators was evolved to coordinate the movements of the different legs. By introducing a staged set of manageable challenges, the algorithm's performance was improved.}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = {robot controller} } @inproceedings{Lindgren92a, author = {Lindgren, K. and Nilsson, A. and Nordahl, M.G. and Rade, I.}, title = {Regular Language Inference Using Evolving Neural Networks}, booktitle = {Proceedings of COGANN-92 International Workshop on Combinations of Genetic Algorithms and Neural Networks}, year = {1992}, editor = {Whitley, L.D. and Schaffer, J.D.}, publisher = {IEEE Computer Society Press}, pages = {75-86}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = {regular language inference} } @inproceedings{Lindgren93a, author = {Lindgren, K. and Nilsson, A. and Nordahl, M.G. and Rade, I.}, title = {Evolving Recurrent Neural Networks}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {55-62}, topology = {recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Littman91, key = {genetic algorithms environment fitness functions dynamic; biological modeling evolution and learning; hillclimbing, cogann ref ERL, Evolutionary reinforcement, non-stationary environment, dynamic , neural networks, connectionism}, author = {Michael L. Littman and David H. Ackley}, title = {Adaptation in Constant Utility Non-Stationary Environments}, booktitle = {Proceedings of the Fourth International Conference on Genetic Algorithms}, year = {1991}, pages = {136-142}, abstract = {Abstract: Environments that vary over time present special challenges to adaptive systems. Although in the worst case there may be no hope of effective adaptation, not all forms of environmental variability need be so disabling. We consider a broad class of non-stationary environments, those which combine a variable *result function* with an invariant *utility function*, and demonstrate via simulation that an adaptive strategy employing both evolution and learning can tolerate a much higher rate of environmental variation than an evolution-only strategy. We suggest that in many cases where stability has previously been assumed, the constant utility non-stationary environment may in fact be a more robust description.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Muhlenbein90a, author = {Heinz M\H{u}hlenbein}, title = {Limitations of Multi-Layer Perceptron Networks - Steps Towards Genetic Neural Networks}, journal = {Parallel Computing}, year = {1990}, volume = {14}, pages = {249-260}, topology = {multi-layered}, network = {multi-layer perceptron}, encoding = { }, evolves = { }, applications = { } } @inproceedings{Muhlenbein92, key = {genetic algorithms connectionism neural networks cogann}, author = {Heinz M\H{u}hlenbein}, title = {Parallel Genetic Algorithms and Neural Networks as Learning Machines}, booktitle = {Parallel computing '91 Proceedings of the International Conference}, year = {1992}, editor = {D. J. Evans and G. R. Joubert and H. Liddell}, publisher = {North-Holland Publishing Co.}, address = {Amsterdam}, pages = {91-103}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @incollection{Muhlenbein89a, key = {connectionism, cogann ref}, author = {Heinz M\H{u}hlenbein and J{\"o}rg Kindermann}, title = {The Dynamics of Evolution and Learning: Towards Genetic Neural Networks}, booktitle = {Connectionism in Perspective}, year = {1989}, editor = {R. Pfeifer and Z. Schreter and F. Fogelman-Soulie and L. Steels}, publisher = {Elsevier Science Publishers B.V. (North-Holland)}, pages = {173-197}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @book{Machado92, key = {genetic algorithms connectionism neural networks cogann}, author = {Ricardo Jose Machado and Armando Freitas da Rocha}, title = {Evolutive Fuzzy Neural Networks}, year = {1992}, publisher = {IEEE}, address = {Piscataway, NJ}, journal = {1992 IEEE INT CONF Fuzzy Syst FUZZ-IEEE}, pages = {493-500}, abstract = {ABSTRACT The Authors describe "h" combination of fuzzy neural networks with genetic algorithms, producing a flexible and powerful learning paradigm, called evolutive learning. Evolutive learning combines as complementary tools both inductive learning through synaptic weight adjustment and deductive learning through the modification of the network topology to obtain the automatic adaptation of system knowledge to the problem domain environment. Algorithms for the development of an evolutive learning machine are presented. A fuzzy criterion based on entropy is proposed to select the architecture for a fuzzy neural network best suited to a specific problem domain.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Maeda92, key = {genetic algorithms connectionism neural networks cogann}, author = {Y. Maeda and Y. Kanata}, title = {A Genetic Algorithm for an Unsupervised Learning of Neural Networks}, journal = {Engineering \& Technology}, year = {1992}, volume = {10}, number = {2}, pages = {1-7}, abstract = {ABSTRACT The Authors deal "it" a genetic algorithm for an unsupervised learning rule of neural networks. The genetic algorithm consists of four operations: selection; reproduction; crossover; and mutation. They look into the learning efficiency of two kinds of the crossover for the unsupervised learning rule. Moreover, they investigate the learning rate with respect to the mutation rate.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @incollection{Mandischer93a, author = {M.~Mandischer}, title = {Representation and Evolution of Neural Networks}, booktitle = {Artificial Neural Nets and Genetic Algorithms Proceedings of the International Conference at Innsbruck, Austria}, year = {1993}, editor = {R.F.~Albrecht and C.R.~Reeves and N.C.~Steele}, publisher = {Springer}, address = {Wien and New York}, pages = {643--649}, topology = {feed-forward}, network = { }, encoding = {indirect, developmental}, evolves = {connectivity}, applications = { } } @inproceedings{Maniezzo93, author = {Maniezzo, V.}, title = {Searching Among Search Spaces: Hastening the Genetic Evolution of Feedforward Neural Networks.}, booktitle = {Artificial Neural Nets and Genetic Algorithms}, year = {1993}, editor = {Albrecht, R.F. and Reeves, C.R. and Steele, N.C.}, publisher = {Springer-Verlag}, pages = {635-643}, topology = {feed-forward}, network = { }, encoding = { }, evolves = { }, applications = { } } @article{Maniezzo94a, author = {Maniezzo, V.}, title = {Genetic Evolution of the Topology and Weight Distribution of Neural Networks}, journal = {IEEE Transactions on Neural Networks}, year = {1994}, volume = {5}, pages = {39-53}, topology = { }, network = { }, encoding = { }, evolves = {connectivity, parameters}, applications = { } } @inproceedings{Maricic90, key = {connectionism}, author = {Borut Maricic and Zoran Nikolov}, title = {GENNET - System for Computer Aided Neural Network Design Using Genetic Algorithms}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1990}, month = {Jan}, address = {Washington, DC}, pages = {I-102 - I-105}, topology = { }, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @article{Marin93, author = {F.J. Marin and F. Sandoval}, title = {Genetic Synthesis of Discrete-Time Recurrent Neural Network}, journal = {New Trends in Neural Computation, Springer-Verlag}, year = {1993}, pages = {179-184}, topology = {recurrent}, network = { }, encoding = { }, evolves = {connectivity}, applications = { } } @inproceedings{Marti92, key = {genetic algorithms, connectionism}, author = {Leonardo Mart{\'i}}, title = {Genetically Generated Neural Networks II: Searching for an Optimal Representation}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, year = {1992}, pages = {II-221 - II-226}, abstract = {ABSTRACT Genetic Algorithms (GAs) make use of an internal representation of a given system in order to perform optimization functions. The actual structural layout of this representation, called a genome, has a crucial impact on the outcome of the optimization process. The purpose of this paper is to study the effects of different internal representations in a GA, which generates neural networks. A second GA was used to optimize the genome structure. This structure produces an optimized system within a shorter time interval.}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @inproceedings{Marti92a, key = {genetic algorithms, connectionism}, author = {Leonardo Mart{\'i}}, title = {Genetically Generated Neural Networks I: Representational Effects}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, organization = {IJCNN-92}, year = {1992}, pages = {IV-537 - IV-542}, abstract = {ABSTRACT This paper studies several applications of genetic algorithms (GAs) within the neural networks field. After generating a robust GA engine, the system was used to generate neural network circuit architectures. This was accomplished by using the GA to determine the weights in a fully interconnected network. The importance of the internal genetic representation was shown by testing different approaches. The effects in speed of optimization of varying the constraints imposed upon the desired network were also studied. It was observed that relatively loose constraints provided results comparable to a fully constrained system. The typeof neural network circuits generated were recurrent competitive fields as described by Grossberg (1982).}, topology = {recurrent}, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @mastersthesis{Mayer93, key = {genetic algorithms connectionism neural networks cogann}, author = {Erik Mayer}, title = {Genetic Algorithm Approach to Neural Network Optimization}, year = {1993}, month = {August}, address = {Toledo, Ohio}, school = {University of Toledo}, type = {Masters Thesis}, topology = { }, network = { }, encoding = { }, evolves = {parameters}, applications = { } } @article{Maynard87, author = {Maynard Smith, J.}, title = {When Learning Guides Evolution}, journal = {Nature}, year = {1987}, volume = {329}, pages = {761-762}, topology = { }, network = { }, encoding = { }, evolves = { }, applications = { } } @article{McDonnell92, key = {genetic algorithms connectionism neural networks cogann evolutionary programming}, author = {John R. McDonnell and Don E. Waagen}, title = {Evolving Neural Network Architecture}, journal = {Proceedings of SPIE - The International Society for Optical Engineering}, year = {1992}, volume = {1766}, pages = {690-701}, publisher = {Society for Optical Engineering}, address = {Bellingham, WA USA}, abstract = {AB