In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm (EA) is proposed in this paper. The definition domain is divided into n-...In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm (EA) is proposed in this paper. The definition domain is divided into n-dimension cubic sub-domains (cell) and each individual locates at an ndimensional cube. Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the different cells (exploration) and subsequently shift the first individual from its initial place to the other individual's cell place. If they are already in the same cell heuristic crossover (exploitation) is used. Cell-shift/heuristic crossover adaptively executes exploration/exploitation search with the vary of genetic diversity. The cell-shift EA has excellent performance in terms of efficiency and efficacy on ten usually used optimization benchmarks when comparing with the recent well-known FEP evolutionary algorithm.展开更多
文摘In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm (EA) is proposed in this paper. The definition domain is divided into n-dimension cubic sub-domains (cell) and each individual locates at an ndimensional cube. Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the different cells (exploration) and subsequently shift the first individual from its initial place to the other individual's cell place. If they are already in the same cell heuristic crossover (exploitation) is used. Cell-shift/heuristic crossover adaptively executes exploration/exploitation search with the vary of genetic diversity. The cell-shift EA has excellent performance in terms of efficiency and efficacy on ten usually used optimization benchmarks when comparing with the recent well-known FEP evolutionary algorithm.