Model-based evolutionary algorithms: a short survey

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...

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Vydáno v:Complex & intelligent systems Ročník 4; číslo 4; s. 283 - 292
Hlavní autoři: Cheng, Ran, He, Cheng, Jin, Yaochu, Yao, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2018
Springer Nature B.V
Témata:
Complexity
Computational Intelligence
Crossovers
Data Structures and Information Theory
Engineering
Evolutionary algorithms
Genetic algorithms
Machine learning
Multiple objective analysis
Optimization
Survey and State of the Art
Model-based evolutionary algorithms
Estimation of distribution algorithms
Surrogate modelling
Inverse modelling
ISSN:2199-4536, 2198-6053
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-018-0080-1