Model-based evolutionary algorithms: a short survey

Ran CHENG, Cheng HE, Yaochu JIN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review


The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given.
Original languageEnglish
Pages (from-to)283-292
Number of pages10
JournalComplex & Intelligent Systems
Issue number4
Early online date7 Aug 2018
Publication statusPublished - Dec 2018
Externally publishedYes


  • Model-based evolutionary algorithms
  • Estimation of distribution algorithms
  • Surrogate modelling
  • Inverse modelling


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