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Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics

Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics

Ka-Chun Wong
Copyright: © 2016 |Pages: 26
ISBN13: 9781466694743|ISBN10: 1466694742|EISBN13: 9781466694750
DOI: 10.4018/978-1-4666-9474-3.ch007
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MLA

Wong, Ka-Chun. "Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics." Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2016, pp. 190-215. https://doi.org/10.4018/978-1-4666-9474-3.ch007

APA

Wong, K. (2016). Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics. In S. Bhattacharyya, P. Banerjee, D. Majumdar, & P. Dutta (Eds.), Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications (pp. 190-215). IGI Global. https://doi.org/10.4018/978-1-4666-9474-3.ch007

Chicago

Wong, Ka-Chun. "Evolutionary Algorithms: Concepts, Designs, and Applications in Bioinformatics." In Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, edited by Siddhartha Bhattacharyya, et al., 190-215. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9474-3.ch007

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Abstract

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.

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