Neural networks and evolutionary computation. I. Hybrid approaches in artificial intelligence

This paper focuses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences. It provides a comprehensive and compact overview of hybrid work done in artificial intelligence, and shows the state of the art of combining artificial neural networks and evolutionary algorithms.<<ETX>>

[1]  H. Schwefel,et al.  Applications of Evolutionary Algorithms , 1993 .

[2]  Yoshua Bengio,et al.  Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  Kemal Oflazer,et al.  Genetic Synthesis of Unsupervised Learning Algorithms , 1993 .

[4]  Thomas Bäck,et al.  Genetic Algorithms and Evolution Strategies - Similarities and Differences , 1990, PPSN.

[5]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[6]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[7]  H. P. Schwefel,et al.  Numerische Optimierung von Computermodellen mittels der Evo-lutionsstrategie , 1977 .

[8]  P. Todd,et al.  Exploring Adaptive Agency I: Theory and Methods for Simulating the Evolution of Learning , 1991 .

[9]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[10]  David H. Sharp,et al.  Scaling, machine learning, and genetic neural nets , 1989 .

[11]  D. Parisi,et al.  Growing neural networks , 1991 .

[12]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[13]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[14]  L. Darrell Whitley,et al.  Optimizing Neural Networks Using FasterMore Accurate Genetic Search , 1989, ICGA.

[15]  Samy Bengio,et al.  Generalization of a Parametric Learning Rule , 1993 .

[16]  Hiroaki Kitano,et al.  Empirical Studies on the Speed of Convergence of Neural Network Training Using Genetic Algorithms , 1990, AAAI.

[17]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[18]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[19]  G. Hartmann,et al.  Parallel Processing in Neural Systems and Computers , 1990 .

[20]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[21]  Peter M. Todd,et al.  Exploring adaptive agency II: simulating the evolution of associative learning , 1991 .

[22]  Eric Mjolsness,et al.  A preliminary analysis of recursively generated networks , 1987 .

[23]  Xin Yao,et al.  A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..

[24]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[25]  Alexis P. Wieland,et al.  Evolving Controls for Unstable Systems , 1991 .

[26]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[27]  Mike Rudnick,et al.  A bibliography of the intersection of genetic search and artificial neural networks , 1990 .

[28]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[29]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[30]  M. Nakagawa,et al.  Supervised learning with artificial selection , 1989, International 1989 Joint Conference on Neural Networks.

[31]  Tariq Samad,et al.  Towards the Genetic Synthesisof Neural Networks , 1989, ICGA.

[32]  N. Dodd,et al.  Optimisation of network structure using genetic techniques , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[33]  Dr. David W. Pearson,et al.  Artificial Neural Nets and Genetic Algorithms , 1995, Springer Vienna.

[34]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[35]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[36]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[37]  William A. Phillips,et al.  A Biologically Supported Error-Correcting Learning Rule , 1991, Neural Computation.

[38]  T.-C. Lee,et al.  SPAN: a neural network that grows , 1989, International 1989 Joint Conference on Neural Networks.

[39]  David H. Sharp,et al.  A connectionist model of development. , 1991, Journal of theoretical biology.

[40]  Thomas P. Caudell,et al.  Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms , 1989, ICGA.

[41]  Richard K. Belew,et al.  Evolution, Learning, and Culture: Computational Metaphors for Adaptive Algorithms , 1990, Complex Syst..

[42]  Amir Atiya.  Learning algorithms for neural networks , 1991 .

[43]  Hugo de Garis.  Brain Building with GenNets , 1990 .

[44]  Ron Meir,et al.  Evolving a learning algorithm for the binary perceptron , 1991 .

[45]  Peter M. Todd,et al.  Exploring Adaptive Agency III: Simulating the Evolution of Habituation and Sensitization , 1990, PPSN.

[46]  R. E. Uhrig,et al.  A stochastic learning algorithm for layered neural networks , 1992 .

[47]  Stewart W. Wilson.  Perceptron redux: emergence of structure , 1990 .

[48]  Michael Scholz.  A Learning Strategy for Neural Networks Based on a Modified Evolutionary Strategy , 1990, PPSN.

[49]  Darrell Whitley,et al.  Applying genetic algorithms to neural network learning , 1989 .

[50]  Robert F. Port,et al.  Fractally configured neural networks , 1991, Neural Networks.

[51]  Geoffrey E. Hinton,et al.  How Learning Can Guide Evolution , 1996, Complex Syst..

[52]  Gerhard Weiß,et al.  Neural networks and evolutionary computation. Part II: hybrid approaches in the neurosciences , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.