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A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

Rajashree Mishra, Kedar Nath Das
Copyright: © 2021 |Pages: 33
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
DOI: 10.4018/978-1-7998-8048-6.ch008
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MLA

Mishra, Rajashree, and Kedar Nath Das. "A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization." Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, IGI Global, 2021, pp. 148-180. https://doi.org/10.4018/978-1-7998-8048-6.ch008

APA

Mishra, R. & Das, K. N. (2021). A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization. In I. Management Association (Ed.), Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (pp. 148-180). IGI Global. https://doi.org/10.4018/978-1-7998-8048-6.ch008

Chicago

Mishra, Rajashree, and Kedar Nath Das. "A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, edited by Information Resources Management Association, 148-180. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8048-6.ch008

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Abstract

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

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