Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7547
Title: | An evolutionary algorithm with species-specific explosion for multimodal optimization |
Authors: | Wong, Ka-Chun Prof. LEUNG Kwong Sak Wong, Man-Hon |
Issue Date: | 2009 |
Source: | Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, pp. 923 - 930 |
Journal: | Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 |
Abstract: | This paper presents an evolutionary algorithm, which we call Evolutionary Algorithm with Species-specific Explosion (EASE), for multimodal optimization. EASE is built on the Species Conserving Genetic Algorithm (SCGA), and the design is improved in several ways. In particular, it not only identifies species seeds, but also exploits the species seeds to create multiple mutated copies in order to further converge to the respective optimum for each species. Experiments were conducted to compare EASE and SCGA on four benchmark functions. Cross-comparison with recent rival techniques on another five benchmark functions was also reported. The results reveal that EASE has a competitive edge over the other algorithms tested. Copyright 2009 ACM. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/7547 |
ISBN: | 978-160558325-9 |
DOI: | 10.1145/1569901.1570027 |
Appears in Collections: | Applied Data Science - Publication |
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