Order-theoretic Analysis of Coevolution Problems: Coevolutionary Statics

We present an order-theoretic framework for analyzing coevolution problems. The framework focuses attention on the underlying problem definition, or statics of coevolution, as opposed to the dynamics of search algorithms. We define a notion of solution for coevolution which generalizes similar solution concepts in GA function optimization and MOO. We then define the ideal test set, a potentially small set of tests which allow us to find the solution set of a problem. One feature of the ideal test set is that we are able to categorize problems by considering its cardinality. We conclude by discussing three issues which commonly arise in coevolution from the point of view of coevolutionary statics, pointing out analytical attacks on these issues.

[1]  Michael Barr,et al.  Category theory for computing science , 1995, Prentice Hall International Series in Computer Science.

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

[3]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[4]  Samson Abramsky,et al.  Domain theory , 1995, LICS 1995.

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

[6]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[7]  Richard K. Belew,et al.  Coevolutionary search among adversaries , 1997 .

[8]  J. Pollack,et al.  Coevolving the "Ideal" Trainer: Application to the Discovery of Cellular Automata Rules , 1998 .

[9]  Jordan B. Pollack,et al.  Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms , 2000, PPSN.

[10]  Jordan B. Pollack,et al.  A Game-Theoretic Approach to the Simple Coevolutionary Algorithm , 2000, PPSN.

[11]  Edward R. Scheinerman Mathematics: A Discrete Introduction , 2000 .

[12]  J. Pollack,et al.  Coevolutionary dynamics in a minimal substrate , 2001 .

[13]  Jordan B. Pollack,et al.  Pareto Optimality in Coevolutionary Learning , 2001, ECAL.

[14]  R. Watson,et al.  Pareto coevolution: using performance against coevolved opponents in a game as dimensions for Pareto selection , 2001 .

[15]  Jordan B. Pollack,et al.  Co-Evolution in the Successful Learning of Backgammon Strategy , 1998, Machine Learning.