New Crossover Scheme for Parallel Distributed Genetic Algorithms
نویسندگان
چکیده
This paper proposes a new crossover method for parallel distributed genetic algorithms (PDGAs). PDGAs with multiple subpopulations provide better solutions than conventional GAs with a single population. The proposed method, including the hybridization crossover and the best combinatorial crossover, is designed to increase the performance of PDGAs. The proposed method, which provides high local search ability in each subpopulation and high global search ability by the migration, is evaluated with four standard test functions. The experimental results show that the proposed method is very effective. keywords: Parallel Distributed Algorithms, Optimization, Genetic Algorithms, Crossover Scheme
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