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Hookes-Jeeves-Based Variant of Memetic Algorithm

Hookes-Jeeves-Based Variant of Memetic Algorithm

Dipti Singh, Kusum Deep
Copyright: © 2016 |Pages: 26
ISBN13: 9781466698857|ISBN10: 1466698853|EISBN13: 9781466698864
DOI: 10.4018/978-1-4666-9885-7.ch004
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MLA

Singh, Dipti, and Kusum Deep. "Hookes-Jeeves-Based Variant of Memetic Algorithm." Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications, edited by Pratiksha Saxena, et al., IGI Global, 2016, pp. 85-110. https://doi.org/10.4018/978-1-4666-9885-7.ch004

APA

Singh, D. & Deep, K. (2016). Hookes-Jeeves-Based Variant of Memetic Algorithm. In P. Saxena, D. Singh, & M. Pant (Eds.), Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications (pp. 85-110). IGI Global. https://doi.org/10.4018/978-1-4666-9885-7.ch004

Chicago

Singh, Dipti, and Kusum Deep. "Hookes-Jeeves-Based Variant of Memetic Algorithm." In Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications, edited by Pratiksha Saxena, Dipti Singh, and Millie Pant, 85-110. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9885-7.ch004

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Abstract

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

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