Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (03): 38-45.doi: 10.12052/gdutxb.210131

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A Multi-factor Evolutionary Algorithm Using Maximum Mean Difference Method

Lai Yu-fang, Wang Zhen-you   

  1. School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2021-09-06 Online:2023-05-25 Published:2023-06-08

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Abstract: The multi-factor evolutionary algorithm (MFEA) is mainly studied and improved. The mixed probability distribution of the offspring population optimized by the maximum mean difference method (MMD) is used as the metric criterion of the algorithm. MFEA-MMD is improved on the basis of the Random Mating Probability matrix (RMP) of MFEA-II algorithm to avoid the influence of negative transfer most common in multi-factor evolutionary algorithms to the greatest extent. The convergence rate of the MFEA-MMD is faster than that of MFEA-II. The operation speed of the algorithm is 29% higher than that of MFEA-II, and the degree of knowledge transfer between tasks is 35% higher than that of MFEA-II.

Key words: multi-factor evolutionary algorithm, maximum mean discrepancy, negative transfer, knowledge transfer

CLC Number: 

  • TP301
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