Abstract
Crossover rate (CR) is a key parameter affecting the operation of differential evolution (DE). According to the different status appear in CR adaptive process, the present paper employs power mean averaging operators to improve the value of CR in appropriate chance and propose a Power Mean based Crossover Rate Adaptive Differential Evolution (PMCRADE). The performance of PMCRADE is evaluated on a set of benchmark problems and is compared with conventional and state-of-the-art DE variants. The results show that PMCRADE is better than, or at least comparable to, the compared DE variants in terms of convergence speed and reliability.
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Li, J., Zhu, W., Zhou, M., Wang, H. (2011). Power Mean Based Crossover Rate Adaptive Differential Evolution. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_5
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DOI: https://doi.org/10.1007/978-3-642-23887-1_5
Publisher Name: Springer, Berlin, Heidelberg
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