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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 2565809, 10 pages
Research Article

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

1Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
2BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA

Received 23 January 2016; Revised 15 April 2016; Accepted 3 May 2016

Academic Editor: Jens Christian Claussen

Copyright © 2016 Leilei Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.