Evolutionary algorithms and their applications to engineering problems

The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseu...

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Bibliographic Details
Published inNeural computing & applications Vol. 32; no. 16; pp. 12363 - 12379
Main Authors Slowik, Adam, Kwasnicka, Halina
Format Journal Article
LanguageEnglish
Published London Springer London 01.08.2020
Springer Nature B.V
Subjects
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Image Processing and Computer Vision
Probability and Statistics in Computer Science
Programming languages
Review Article
Evolution strategy
Evolutionary programming
Genetic algorithm
Genetic programming
Real-life applications
Differential evolution
Nature-inspired methods
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Summary:The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04832-8