Paper Info
Title
An evolutionary algorithm with species-specific explosion for multimodal optimization
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.
Year
DOI
Venue
2009
10.1145/1569901.1570027
GECCO
Keywords
Field
DocType
species conserving genetic algorithm,recent rival technique,competitive edge,species-specific explosion,benchmark function,multimodal optimization,evolutionary algorithm,species seed,genetic algorithm
Mathematical optimization,Evolutionary algorithm,Computer science,Competitive advantage,Meta-optimization,Exploit,Function optimization,Artificial intelligence,Machine learning,Genetic algorithm
Conference
Citations 
PageRank 
References 
8
0.58
13
Authors
3
Name
Order
Citations
PageRank
Ka-Chun Wong129140.18
Kwong-Sak Leung21887205.58
Man Hon Wong3814233.13