A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Two algorithmic enhancements for the parallel differential evolution
International Journal of Innovative Computing and Applications
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An approach for estimating separability and its application on high dimensional optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Fixed-point digital IIR filter design using two-stage ensemble evolutionary algorithm
Applied Soft Computing
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Large scale global optimization (LSGO) is a very important and extremely difficult task in optimization domain, which is urgently needed for scientific and engineering applications. Recently, decompose-and-conquer strategy has become a promising method to handle LSGO problems. In this paper, we propose a new strategy variance priority (VP) to improve the classical cooperative co-evolution framework. Based on this proposed strategy, a new LSGO algorithm, variance priority based cooperative co-evolution differential evolution (VP-DECC), is developed. The advantages of VP strategy over the other decompose-and-conquer strategies are experimentally investigated. Especially, it has shown excellent performance in dealing with more complex problems.