1 Evolving Heterogeneous Neural Agents by Local Selection

Evolutionary algorithms have been applied to the synthesis of neural architectures, but they normally lead to uniform populations. Homogeneous solutions, however, are inadequate for certain applications and models. For these cases, local selection may produce the desired heterogeneity in the evolving neural networks. This chapter describes algorithms based on local selection, and discusses the main differences distinguishing them from standard evolutionary algorithms. The use of local selection to evolve neural networks is illustrated by surveying previous work in three domains (simulations of adaptive behavior, realistic ecological models, and browsing information agents), as well as reporting on new results in feature selection for classification.

[1]  Kenneth A. De Jong,et al.  On Decentralizing Selection Algorithms , 1995, ICGA.

[2]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[3]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[4]  Filippo Menczer,et al.  Local Selection , 1998, Evolutionary Programming.

[5]  John E. Moody,et al.  The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems , 1991, NIPS.

[6]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[7]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[8]  F. Menczer,et al.  Co-evolution of movement behaviours by tropical pelagic predatory fishes in response to prey environment: a simulation model , 2000 .

[9]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[10]  William Nick Street,et al.  A Neural Network Model for Prognostic Prediction , 1998, ICML.

[11]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[12]  Larry J. Eshelman,et al.  Crossover's Niche , 1993, ICGA.

[13]  Frédéric Gruau,et al.  Genetic synthesis of Boolean neural networks with a cell rewriting developmental process , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[14]  Giles,et al.  Searching the world wide Web , 1998, Science.

[15]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[16]  Filippo Menczer,et al.  Life-like agents: internalizing local cues for reinforcement learning and evolution , 1998 .

[17]  Kevin Baker,et al.  Classification of radar returns from the ionosphere using neural networks , 1989 .

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

[19]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[20]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[21]  Domenico Parisi,et al.  Recombination and unsupervised learning: effects of crossover in the genetic optimization of neural networks , 1992 .

[22]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[23]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[24]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[25]  Filippo Menczer,et al.  ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery , 1997, ICML 1997.

[26]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[27]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[28]  Samir W. Mahfoud.  A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[29]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[30]  P. Langley.  Selection of Relevant Features in Machine Learning , 1994 .

[31]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[32]  Filippo Menczer,et al.  Scalable Web Search by Adaptive Online Agents: An InfoSpiders Case Study , 1999 .

[33]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[34]  John Michael Neal McInerney,et al.  Biologically influenced algorithms and parallelism in non-linear optimization , 1992 .

[35]  David W. Opitz,et al.  Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies , 1997, J. Artif. Intell. Res..

[36]  H. Fawcett.  Manual of Political Economy , 1995 .

[37]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[38]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[39]  Filippo Menczer,et al.  From Complex Environments to Complex Behaviors , 1996, Adapt. Behav..

[40]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[42]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[43]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[44]  Filippo Menczer,et al.  Latent energy environments , 1996 .

[45]  Filippo Menczer,et al.  EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY , 1994 .

[46]  David J. Chalmers,et al.  The Evolution of Learning: An Experiment in Genetic Connectionism , 1991 .

[47]  Samir W. Mahfoud.  Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization , 1993, ICGA.

[48]  Filippo Menczer,et al.  Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms , 2000, Evolutionary Computation.

[49]  Paul S. Bradley,et al.  Feature Selection via Mathematical Programming , 1997, INFORMS J. Comput..

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

[51]  William Nick Street,et al.  Breast Cancer Diagnosis and Prognosis Via Linear Programming , 1995, Oper. Res..

[52]  Morgan B Kaufmann,et al.  Finite Markov Chain Analysis of Genetic Algorithms with Niching , 1993 .

[53]  D. Hartl,et al.  Principles of population genetics , 1981 .

[54]  L. Darrell Whitley,et al.  COGANN-92 : International Workshop on Combinations of Genetic Algorithms and Neural Networks, June 6, 1992 Baltimore, Maryland , 1992 .