Abstract
Evolutionary algorithms (EAs) are global, parallel, search and optimization methods, founded on the principles of natural selection and population genetics. In general, any iterative, population based approach that uses selection and random variation to generate new solutions can be regarded as an EA. The evolutionary algorithms field has its origins in four landmark evolutionary approaches: evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), and genetic programming (GP). The genetic algorithm was popularized by Goldberg (1989) and, as a result, the majority of control applications in the literature adopt this approach.
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© 2013 Springer-Verlag Berlin Heidelberg
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Ünal, M., Ak, A., Topuz, V., Erdal, H. (2013). Genetic Algorithm. In: Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Studies in Computational Intelligence, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32900-5_3
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DOI: https://doi.org/10.1007/978-3-642-32900-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32899-2
Online ISBN: 978-3-642-32900-5
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