Evolutionary computation meets machine learning: A survey

Jun Zhang, Zhi Hui Zhang, Ying Lin, Ni Chen, Yue Jiao Gong, Jing Hui Zhong, Henry S.H. Chung, Yun Li, Yu Hui Shi

Research output: Contribution to journalReview article

136 Citations (Scopus)

Abstract

Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. SI algorithms share many common characteristics with EAs and are also regarded to be in the EC algorithm family. The new population is then evaluated again and the iteration continues until a termination criterion is satisfied. ML is one of the most promising and salient research areas in artificial intelligence, which has experienced a rapid development and has become a powerful tool in a wide range of applications. In many applications, EC algorithms incorporating ML techniques have been proven to be advantageous in both convergence speed and solution quality. The survey is organized from the EC perspective, including population initialization, fitness evaluation and selection, population reproduction and variation, algorithm adaptation, and local search.

Original languageEnglish
Article number6052374
Pages (from-to)68-75
Number of pages8
JournalIEEE Computational Intelligence Magazine
Volume6
Issue number4
DOIs
Publication statusPublished - 20 Oct 2011

Keywords

  • data analysis
  • evolutionary computation
  • iterative methods
  • learning (artificial intelligence)
  • search problems

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    Zhang, J., Zhang, Z. H., Lin, Y., Chen, N., Gong, Y. J., Zhong, J. H., Chung, H. S. H., Li, Y., & Shi, Y. H. (2011). Evolutionary computation meets machine learning: A survey. IEEE Computational Intelligence Magazine, 6(4), 68-75. [6052374]. https://doi.org/10.1109/MCI.2011.942584