Cooperative coevolutionary algorithms (CCEAs) divide a given problem in to a number of subproblems and use an evolutionary algorithm to solve each subproblem. This short paper is concerned with the scenario under which only a single, global 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.

翻译：合作协同进化算法（CCEAs）将给定问题划分为若干子问题，并使用进化算法分别求解每个子问题。本文聚焦于仅存在单一全局适应度测量的情形。通过移除通常采用的子问题配对机制，提出此类CCEAs可视为利用了早期进化策略中提出的全局交叉算子的广义形式。基于著名的NK适应度景观模型，本文探究了全局交叉的不同变化参数对底层适应度景观崎岖性的影响。实验结果表明，相较于最广泛使用的CCEAs形式，该方案具有改进效果，该结论通过其他经典测试函数得到了进一步验证。