Loosely Coupled Distributed Genetic Algorithms

Iterated, noncooperative N-person games with limited interaction are considered. Each player in the game has defined its local payoff function and a set of strategies. While each player acts to maximize its payoff, we are interested in a global behavior of the team of players measured by the average payoff received by the team. To study behavior of the system we propose a new parallel and distributed genetic algorithm based on evaluation of local fitness functions while the global criterion is optimized. We present results of simulation study which support our ideas.

[1]  Jeffrey S. Rosenschein,et al.  A game theoretic approach to distributed artificial intelligence and the pursuit problem (abstract) , 1992, SIGO.

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

[3]  P. Ordeshook.  Game theory and political theory: Contents , 1986 .

[4]  Bjørn Lomborg,et al.  Game Theory Versus Multiple Agents: The Iterated Prisoner's Dilemma , 1992, MAAMAW.

[5]  J. Nash.  Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[7]  Alan H. Bond,et al.  Readings in Distributed Artificial Intelligence , 1988 .

[8]  Franciszek Seredynski.  Dynamic mapping and load balancing with parallel genetic algorithms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[9]  Jeffrey S. Rosenschein,et al.  Cooperation without Communication , 1986, AAAI.

[10]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[11]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

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

[13]  A. Rapoport.  Optimal policies for the Prisoner's Dilemma. , 1967, Psychological review.

[14]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[15]  N. Meyers,et al.  H = W. , 1964, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Joachim Stender,et al.  Parallel Genetic Algorithms: Introduction and Overview of Current Research , 1993 .

[17]  D. Hofstadter.  Metamagical Themas: Questing for the Essence of Mind and Pattern , 1985 .

[18]  David B. Fogel,et al.  Evolving Behaviors in the Iterated Prisoner's Dilemma , 1993, Evolutionary Computation.