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Comparison and Analysis of Competition Strategies in Competitive Coevolutionary Algorithms  

Kim, Yeo Keun (Department of Industrial Engineering, Chonnam National University)
Kim, Jae Yun (Department of Industrial Engineering, Chonnam National University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.28, no.1, 2002 , pp. 87-98 More about this Journal
Abstract
A competitive coevolutionary algorithm is a probabilistic search method that imitates coevolution process through evolutionary arms race. The algorithm has been used to solve adversarial problems. In the algorithms, the selection of competitors is needed to evaluate the fitness of an individual. The goal of this study is to compare and analyze several competition strategies in terms of solution quality, convergence speed, balance between competitive coevolving species, population diversity, etc. With two types of test-bed problems, game problems and solution-test problems, extensive experiments are carried out. In the game problems, sampling strategies based on fitness have a risk of providing bad solutions due to evolutionary unbalance between species. On the other hand, in the solution-test problems, evolutionary unbalance does not appear in any strategies and the strategies using information about competition results are efficient in solution quality. The experimental results indicate that the tournament competition can progress an evolutionary arms race and then is successful from the viewpoint of evolutionary computation.
Keywords
coevolutionary algorithm; competitive coevolution; competition strategy; evolutionary arms race;
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