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：协同协同进化算法（CCEAs）将给定问题分解为若干子问题，并使用进化算法分别求解每个子问题。这篇短论文关注的是仅存在单一全局适应度度量的场景。通过移除通常使用的子问题配对机制，本文提出此类CCEAs可视为利用了早期进化策略中引入的全局交叉算子的一种广义版本。借助著名的适应度景观NK模型，本文探讨了全局交叉在不同方面对底层适应度景观崎岖度的影响效应。结果表明，该方法相较于最广泛使用的CCEAs形式有所改进，其他著名测试函数的进一步实验也证实了这一点。