By Topic

Machine learning using a genetic algorithm to optimise a draughts program board evaluation function

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
K. J. Chisholm ; Dept. of Comput. Studies, Napier Univ. of Edinburgh, UK ; P. V. G. Bradbeer

The paper reviews the authors' work in using a genetic algorithm (GA) to optimise the board evaluation function of a game playing program. The test bed used for this study has been the game of draughts (checkers). A pool of draughts programs are played against each other in a round robin (all-play-all) tournament to evaluate the fitness of each `player' and a GA is used to preserve and improve the best performers. Some solutions to the problems of attempting to compare the absolute performance of possible solutions in this area which is mainly about relative abilities are presented. Comparisons with classical methods and results are also briefly discussed

Published in:

Evolutionary Computation, 1997., IEEE International Conference on

Date of Conference:

13-16 Apr 1997