Environmental effects on the coevolution of pursuit and evasion strategies

The game of tag is frequently used in the study of pursuit and evasion strategies that are discovered through competitive coevolution. The aim of coevolution is to create an arms race where opposing populations cyclically evolve in incremental improvements, driving the system towards better strategies. A coevolutionary simulation of the game of tag involving two populations of agents; pursuers and evaders, is developed to investigate the effects of a boundary and two obstacles. The evolution of strategies through Chemical Genetic Programming optimizes the mapping of genotypic strings to phenotypic trees. Four experiments were conducted, distinguished by speed differentials and environmental conditions. Designing experiments to evaluate the efficacy of emergent strategies often reveal necessary steps needed for coevolutionary progress. The experiments that excluded obstacles and boundaries provided design pointers to ensure coevolutionary progress as well as a deeper understanding of strategies that emerged when obstacles and boundaries were added. In the latter, we found that an awareness of the environment and the pursuer was not critical in an evader’s strategy to survive, instead heading to the edge of the boundary or behind an obstacle in a bid to ‘throw-off or hide from the pursuer’ or simply turn in circles was often sufficient, thereby revealing possible suboptimal strategies that were environment specific. We also observed that a condition for coevolutionary progress was that the problem complexity must be surmountable by at least one population; that is, some pursuer must be able to tag an opponent. Due to the use of amino-acid building blocks in our Chemical Genetic Program, our simulations were able to achieve significant complexity in a short period of time.

[1]  M. O'Neill,et al.  Grammatical evolution , 2001, GECCO '09.

[2]  J. Pollack,et al.  Challenges in coevolutionary learning: arms-race dynamics, open-endedness, and medicocre stable states , 1998 .

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

[4]  Wojciech Piaseczny,et al.  Chemical genetic programming - evolution of amino acid rewriting rules used for genotype-phenotype translation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Robert E. Smith,et al.  An application of genetic algorithms to air combat maneuvering , 1997 .

[6]  Jeffrey K. Bassett,et al.  An Analysis of Cooperative Coevolutionary Algorithms A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University , 2003 .

[7]  Wojciech Piaseczny,et al.  Chemical Genetic Programming - Coevolution Between Genotypic Strings and Phenotypic Trees , 2004, GECCO.

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

[9]  Hidefumi Sawai,et al.  Chemical genetic algorithms: coevolution between codes and code translation , 2002 .

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

[11]  Edwin D. de Jong,et al.  Learning the Ideal Evaluation Function , 2003, GECCO.

[12]  Karl Sims,et al.  Evolving 3d morphology and behavior by competition , 1994 .

[13]  Craig W. Reynolds Competition, Coevolution and the Game of Tag , 1994 .

[14]  Wojciech Piaseczny,et al.  Chemical Genetic Programming – The Effect of Evolving Amino Acids , 2004 .