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A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Joint Authors
Cao, Leilei
Xu, Lihong
Goodman, Erik D.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-18
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed.
Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages.
In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual.
The current best individual served as a guide to attract offspring to its region of genotype space.
Mutation was added to offspring according to a dynamic mutation probability.
To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search.
Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
American Psychological Association (APA)
Cao, Leilei& Xu, Lihong& Goodman, Erik D.. 2016. A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099603
Modern Language Association (MLA)
Cao, Leilei…[et al.]. A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1099603
American Medical Association (AMA)
Cao, Leilei& Xu, Lihong& Goodman, Erik D.. A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099603
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099603