Genetic algorithms: theory, genetic operators, solutions, and applications

A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills t...

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Bibliographic Details
Published in:Evolutionary intelligence Vol. 17; no. 3; pp. 1245 - 1256
Main Authors: Alhijawi, Bushra, Awajan, Arafat
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024
Springer Nature B.V
Subjects:
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Biological activity
Chromosomes
Control
Engineering
Evolutionary algorithms
Genes
Genetic algorithms
Mathematical and Computational Engineering
Mechatronics
Mutation
Operators
Optimization
Review Article
Robotics
Search process
Statistical Physics and Dynamical Systems
GA
Evolutionary algorithm
Optimization
Genetic algorithms
Genetic operators
ISSN:1864-5909, 1864-5917
Online Access:Get full text
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Summary:A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field.
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ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-023-00822-6