Evolutionary Computation Meets Machine Learning: A Survey
Jun Zhang, Zhi‐Hui Zhan, Ying Lin, Ni Chen, Yue‐Jiao Gong, Jinghui Zhong, Henry Shu-Hung Chung, Yun Li, Yuhui Shi · IEEE Computational Intelligence Magazine · 2011
Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.