渡线
共同进化
健身景观
进化算法
数学优化
计算机科学
操作员(生物学)
进化计算
度量(数据仓库)
全局优化
数学
人工智能
生态学
人口
生物化学
化学
人口学
抑制因子
数据库
社会学
转录因子
基因
生物
标识
DOI:10.1109/tevc.2024.3355776
摘要
Cooperative coevolutionary algorithms (CCEAs) divide a given problem in to a number of subproblems and use an evolutionary algorithm to solve each subproblem. This letter is concerned with the scenario under which a single fitness measure exists. By removing the typically used subproblem partnering mechanism, it is suggested that such CCEAs can be viewed as making use of a generalised version of the global crossover operator introduced in early Evolution Strategies. Using the well-known NK model of fitness landscapes, the effects of varying aspects of global crossover with respect to the ruggedness of the underlying fitness landscape are explored. Results suggest improvements over the most widely used form of CCEAs, something further demonstrated using other well-known test functions.
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