Session:

Evolutionary Computation and Neural Networks (ECNN)
Monday March 01, 2004, 11.40 – 12.00

Session Chair:

Paulo Cortez, Miguel Rocha

Paper Title:

Evolving Strategy for Game Playing

Author(s):

J. Hynek, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic

Abstract:

This paper examines genetic algorithm and machine learning using the game of Nim. We have studied various attempts to evolve a competitive or even optimal strategy for this game that have been undertaken before. Based on these findings we have reviewed them and then we have designed a new approach that has been tested on a particular version of the game of Nim. Contrary to the evolving populations of “hosts” and “parasites”, we have proposed a solution that is based on a genetic algorithm utilizing single population only. Moreover, we have exploited a kind of macromutation operator previously utilized within the field of genetic programming. The so-called headless chicken crossover helped us to significantly speed up the evolutionary process. We have carried out series of experiments and the analysis of these experiments is presented here. We do believe that the approaches and results described here can be useful when tackling other problems where the suitable strategy goal is pursued.