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The cooperative co-evolution framework (CC) is widely used in the large scale global optimization. It is believed that the CC framework is very sensitive to grouping strategies and the performance deteriorate if interacted variables are not correctly grouped. So many efforts have been devoted to find good ways to correctly decompose the large scale problem into smaller sub-problems so as to effectively...
Cooperative co-evolution framework is widely used in large scale optimization problems. Usually, the large scale problem is divided into smaller sub groups using black-box decomposition methods based on variable interactions. However these black-box decomposition methods have limitations in finding correct variable interactions. In this paper, a white-box decomposition method named formula based grouping...
Wide study and application exposes some problems of evolutionary algorithms such as premature convergence and poor performance in convergence. In order to overcome these issues, this paper proposes an adaptive co-evolutionary algorithm based on genotypic diversity measure, where adaptive selection, mutation and substitution operators are designed to realize cooperative search among operators and dynamic...
In this paper, a novel evolutionary algorithm framework called smoothing and auxiliary functions based cooperative coevolution (Briefly, SACC) for large scale global optimization problems is proposed. In this new algorithm pattern, a smoothing function and an auxiliary function are well integrated with a cooperative coevolution algorithm. In this way, the performance of the cooperative coevolution...
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