Model-based evolutionary algorithms: a short survey View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Ran Cheng, Cheng He, Yaochu Jin, Xin Yao

ABSTRACT

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. More... »

PAGES

283-292

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40747-018-0080-1

DOI

http://dx.doi.org/10.1007/s40747-018-0080-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106033207


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