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On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution

机译:合作协同分解中可分离度与演化深度的关系

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Problem decomposition determines how subcomponents are created that have a vital role in the performance of cooperative coevolution. Cooperative coevolution naturally appeals to fully separable problems that have low interaction amongst subcomponents. The interaction amongst subcomponents is defined by the degree of separability. Typically, in cooperative coevolution, each subcomponent is implemented as a sub-population that is evolved in a round-robin fashion for a specified depth of evolution. This paper examines the relationship between the depth of evolution and degree of separability for different types of global optimisation problems. The results show that the depth of evolution is an important attribute that affects the performance of cooperative coevolution and can be used to ascertain the nature of the problem in terms of the degree of separability.
机译:问题分解决定了如何创建在协作协同进化中起关键作用的子组件。合作协同进化自然吸引了完全可分离的问题,这些问题之间子组件之间的交互作用很低。子组件之间的交互作用由可分离程度定义。通常,在协作式协同进化中,每个子组件都被实现为一个子种群,该子种群以循环方式进化了指定的进化深度。本文研究了不同类型的全局优化问题的演化深度与可分离度之间的关系。结果表明,进化深度是影响合作协同进化性能的重要属性,可用于根据可分离性程度确定问题的性质。

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