Ensemble of evolution algorithm based on self-adaptive learning population search techniques

XUE Yu, ZHUANG Yi, XU Bin, ZHANG You-yi

Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (2) : 458-465.

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Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (2) : 458-465. DOI: 10.12011/1000-6788(2014)2-458

Ensemble of evolution algorithm based on self-adaptive learning population search techniques

  • XUE Yu1, ZHUANG Yi1, XU Bin1, ZHANG You-yi2
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Abstract

In order to enhance the performance of universality and robustness of the numerical optimization algorithms, an ensemble of evolution algorithm based on self-adaptive learning population search techniques (EEA-SLPS) is proposed. EEA-SLPS integrates three self-adaptive learning population based algorithms from different fields of stochastic search techniques. One sub-algorithm is designed in this paper, the other two are recently proposed in relevant literature. The whole individual population is divided into three sub-populations, and each sub-algorithm is employed to evolve each sub-population respectively in parallel manner during the whole search process. In each sub-algorithm, both search strategies and parameters are gradually self-adaptive in different stages of the search process. The performance of EEA-SLPS is extensively evaluated on a suite of 26 bound-constrained test functions with different characteristics. By comparing with several state-of-the-art algorithms, the experimental results clearly verify the advantages of EEA-SLPS.

Key words

self-adaptation / ensemble evolution / evolution learning / computational intelligence / optimization

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XUE Yu , ZHUANG Yi , XU Bin , ZHANG You-yi. Ensemble of evolution algorithm based on self-adaptive learning population search techniques. Systems Engineering - Theory & Practice, 2014, 34(2): 458-465 https://doi.org/10.12011/1000-6788(2014)2-458

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