New Methods for Competitive Coevolution

We consider competitive coevolution, in which fitness is based on direct competition among individuals selected from two independently evolving populations of hosts and parasites. Competitive coevolution can lead to an arms race, in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. Competitive fitness sharing changes the way fitness is measured; shared sampling provides a method for selecting a strong, diverse set of parasites; and the hall of fame encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift.

[1]  C. Mode.  A MATHEMATICAL MODEL FOR THE CO‐EVOLUTION OF OBLIGATE PARASITES AND THEIR HOSTS , 1958 .

[2]  P. Raven,et al.  BUTTERFLIES AND PLANTS: A STUDY IN COEVOLUTION , 1964 .

[3]  L. V. Valen,et al.  A new evolutionary law , 1973 .

[4]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[5]  J. Roughgarden.  Resource partitioning among competing species--a coevolutionary approach. , 1976, Theoretical population biology.

[6]  J. Krebs,et al.  Arms races between and within species , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  D. Janzen.  WHEN IS IT COEVOLUTION? , 1980, Evolution; international journal of organic evolution.

[8]  E. Berlekamp,et al.  Winning Ways for Your Mathematical Plays , 1983 .

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

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

[11]  Michael Kearns,et al.  On the complexity of teaching , 1991, COLT '91.

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

[13]  John Shawe-Taylor,et al.  On exact specification by examples , 1992, COLT '92.

[14]  Peter J. Angeline,et al.  Competitive Environments Evolve Better Solutions for Complex Tasks , 1993, ICGA.

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

[16]  Melanie Mitchell,et al.  Genetic Algorithms and Artiicial Life , 1993 .

[17]  S. Fe.  Genetic Algorithms and Arti cial Life , 1993 .

[18]  Chuen-Tsai Sun,et al.  Genetic algorithm learning in game playing with multiple coaches , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[19]  Samir W. Mahfoud.  Population Size and Genetic Drift in Fitness Sharing , 1994, FOGA.

[20]  Melanie Mitchell,et al.  Genetic algorithms and artificial life , 1994 .

[21]  Filippo Neri,et al.  Search-Intensive Concept Induction , 1995, Evolutionary Computation.

[22]  Dave Cliff,et al.  Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations , 1995, ECAL.

[23]  Richard K. Belew,et al.  Methods for Competitive Co-Evolution: Finding Opponents Worth Beating , 1995, ICGA.

[24]  Richard K. Belew,et al.  A competitive approach to game learning , 1996, COLT '96.

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

[26]  Jean-Arcady Meyer,et al.  Evolution and development of neural controllers for locomotion, gradient-following, and obstacle-avoidance in artificial insects , 1998, IEEE Trans. Neural Networks.

[27]  Terry Bossomaier,et al.  Evolution of Neural Networks to Play the Game of Dots-and-Boxes , 1998, ArXiv.

[28]  David S. Goodsell,et al.  Computational Coevolution of Antiviral Drug Resistance , 1997, Artificial Life.