Genetic Algorithms and Artificial Life

Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

[1]  Hugues Bersini,et al.  The Immune Recruitment Mechanism: A Selective Evolutionary Strategy , 1991, ICGA.

[2]  Kenneth de Jong,et al.  Genetic-algorithm-based learning , 1990 .

[3]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

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

[5]  P E Seiden,et al.  A computer model of cellular interactions in the immune system. , 1992, Immunology today.

[6]  Kristian Lindgren,et al.  Evolutionary phenomena in simple dynamics , 1992 .

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

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

[9]  L. Darrell Whitley,et al.  Genetic Reinforcement Learning with Multilayer Neural Networks , 1991, ICGA.

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

[11]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[12]  L. Buss,et al.  The evolution of individuality , 1987 .

[13]  Thomas S. Ray,et al.  Is It Alive or Is It GA? , 1991, ICGA.

[14]  David H. Ackley,et al.  Interactions between learning and evolution , 1991 .

[15]  Larry J. Eshelman,et al.  On Crossover as an Evolutionarily Viable Strategy , 1991, ICGA.

[16]  Inman Harvey The Puzzle of the Persistent Question Marks : A Case Study of Genetic Drift , 1993, ICGA.

[17]  Renaud Dumeur,et al.  Extended classifiers for simulation of adaptive behavior , 1991 .

[18]  David R. Jefferson,et al.  RAM: Artificial Life for the Exploration of Complex Biological Systems , 1987, IEEE Symposium on Artificial Life.

[19]  Lashon B. Booker,et al.  Instinct as an inductive bias for learning behavioral sequences , 1991 .

[20]  Stephanie Forrest,et al.  An Introduction to SFI Echo , 1993 .

[21]  Marco Dorigo,et al.  Alecsys: A Parallel Laboratory for Learning Classifier Systems , 1991, ICGA.

[22]  Stewart W. Wilson The Genetic Algorithm and Simulated Evolution , 1987, ALIFE.

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

[24]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[25]  Filippo Menczer,et al.  A model for the emergence of sex in evolving networks: Adaptive advantage or random drift? In F , 1992 .

[26]  Lashon B. Booker,et al.  Proceedings of the fourth international conference on Genetic algorithms , 1991 .

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

[28]  Rick L. Riolo,et al.  Modeling Simple Human Category Learning with a Classifier System , 1991, International Conference on Genetic Algorithms.

[29]  John J. Grefenstette,et al.  How Genetic Algorithms Work: A Critical Look at Implicit Parallelism , 1989, ICGA.

[30]  Ron R. Hightower,et al.  The Evolution of Secondary Organization in Immune System Gene Libraries , 1993 .

[31]  David Rogers Weather prediction using a genetic memory , 1990 .

[32]  Reinhard Männer,et al.  Parallel Problem Solving from Nature 2, PPSN-II, Brussels, Belgium, September 28-30, 1992 , 1992, Parallel Problem Solving from Nature.

[33]  Yuval Davidor,et al.  Genetic algorithms and robotics , 1991 .

[34]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[35]  Alan S. Perelson,et al.  Using Genetic Algorithms to Explore Pattern Recognition in the Immune System , 1993, Evolutionary Computation.

[36]  Stewart W. Wilson Knowledge Growth in an Artificial Animal , 1985, ICGA.

[37]  S Forrest,et al.  Genetic algorithms , 1996, CSUR.

[38]  John R. Koza,et al.  Genetic evolution and co-evolution of computer programs , 1991 .

[39]  R. Belew Interposing an ontogenic model between Genetic Algorithms and Neural Networks , 1992 .

[40]  Piero Mussio,et al.  Toward a Practice of Autonomous Systems , 1994 .

[41]  Steffen Schulze-Kremer,et al.  Genetic Algorithms for Protein Tertiary Structure Prediction , 1993, ECML.

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

[43]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[44]  In Schoenauer,et al.  Parallel Problem Solving from Nature , 1990, Lecture Notes in Computer Science.

[45]  W. Brian Arthur,et al.  On designing economic agents that behave like human agents , 1993 .

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

[47]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[48]  R. Axelrod An Evolutionary Approach to Norms , 1986, American Political Science Review.

[49]  Lynn Nadel,et al.  1990 Lectures in Complex Systems , 1991 .

[50]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[51]  John H. Miller,et al.  The coevolution of automata in the repeated Prisoner's Dilemma , 1996 .

[52]  John J. Grefenstette,et al.  Lamarckian Learning in Multi-Agent Environments , 1991, ICGA.

[53]  Robert E. Marks,et al.  Breeding hybrid strategies: optimal behaviour for oligopolists , 1989, ICGA.

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

[55]  Stephanie Forrest,et al.  Analogies with immunology represent an important step toward the vision of robust, distributed protection for computers. , 1991 .

[56]  Inman Harvey,et al.  Issues in evolutionary robotics , 1993 .

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

[58]  Rick L. Riolo,et al.  Lookahead planning and latent learning in a classifier system , 1991 .

[59]  Alex Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers I. Introduction , 1957 .

[60]  John Maynard Smith,et al.  When learning guides evolution , 1987, Nature.

[61]  Dario Floreano,et al.  From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior , 2000, Journal of Cognitive Neuroscience.

[62]  Ron Meir,et al.  The Effect of Learning on the Evolution of Asexual Populations , 1990, Complex Syst..

[63]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[64]  Stanley J. Rosenschein,et al.  From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior , 1996 .

[65]  M. Bedau Measurement of Evolutionary Activity, Teleology, and Life , 1996 .

[66]  A. Perelson Immune Network Theory , 1989, Immunological reviews.

[67]  Lashon B. Booker,et al.  Intelligent Behavior as an Adaptation to the Task Environment , 1982 .

[68]  Melanie Mitchell,et al.  Evolving cellular automata to perform computations: mechanisms and impediments , 1994 .

[69]  David R. Jefferson,et al.  Selection in Massively Parallel Genetic Algorithms , 1991, ICGA.

[70]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[71]  J. Baldwin A New Factor in Evolution , 1896, The American Naturalist.

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

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

[74]  J. David Schaffer,et al.  Proceedings of the third international conference on Genetic algorithms , 1989 .

[75]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[76]  Jean-Arcady Meyer,et al.  Extended classifiers for simulation of adaptive behavior , 1991 .

[77]  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.

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

[79]  Thomas S. Ray,et al.  An Approach to the Synthesis of Life , 1991 .

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

[81]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[82]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[83]  Martin Zwick,et al.  Dynamics of Diversity in an Evolving Population , 1992, PPSN.

[84]  Alan S. Perelson,et al.  Theoretical and Experimental Insights into Immunology , 1992, NATO ASI Series.

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

[86]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[87]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[88]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[89]  John H. Miller,et al.  Two essays on the economics of imperfect information , 1988 .

[90]  George E. P. Box,et al.  Evolutionary Operation: a Method for Increasing Industrial Productivity , 1957 .

[91]  M. Feldman,et al.  Recombination dynamics and the fitness landscape , 1992 .

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

[93]  G. M. Werner Evolution of Communication in Artificial Organisms, Artifial Life II , 1991 .

[94]  As Fraser,et al.  Simulation of Genetic Systems by Automatic Digital Computers II. Effects of Linkage on Rates of Advance Under Selection , 1957 .