Evolving speciated checkers players with crowding algorithm

Kyung Joong Kim, Sung Bae Cho

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

Conventional evolutionary algorithms have a property that only one solution often dominates and it is sometimes useful to find diverse solutions and combine them because there might be many different solutions to one problem in real-world problems. Recently, developing checkers players using evolutionary algorithms has been widely exploited to show the power of evolution for machine learning. In this paper, we propose an evolutionary checkers player that is developed by a speciation technique called the "crowding algorithm". In many experiments, our checkers player with an ensemble structure showed better performance than non-speciated checkers players. A neural network is used to validate the game board, and a min-max search finds the optimal board. The neural network evaluator is evolved using the evolutionary algorithm.

Original languageEnglish
Pages407-412
Number of pages6
DOIs
Publication statusPublished - 2002 Jan 1
Event2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States
Duration: 2002 May 122002 May 17

Other

Other2002 Congress on Evolutionary Computation, CEC 2002
CountryUnited States
CityHonolulu, HI
Period02/5/1202/5/17

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Kim, K. J., & Cho, S. B. (2002). Evolving speciated checkers players with crowding algorithm. 407-412. Paper presented at 2002 Congress on Evolutionary Computation, CEC 2002, Honolulu, HI, United States. https://doi.org/10.1109/CEC.2002.1006269