Abstract
For many evolutionary algorithms a key obstacle to finding the global optima is insufficient solution diversity, causing the algorithm to become mired in a local optimum. Solution diversity can be influenced by algorithm parameters including population size, mutation operator and diversity preservation techniques. This study examines the combined effect of population size, mutation value and the geography imposed by the combinatorial graphs on a set of five standard evolutionary algorithm problems. a tradeoff can be seen between the initial diversity of the population size, introduction of new diversity from mutation, and the preservation of diversity from combinatorial graph. with an appropriate fusion of these three factors a level of diversity can be achieved to decrease the time to find the global optima. © 2010 IEEE.
Recommended Citation
J. Jayachandran and S. Corns, "A Comparative Study of Diversity in Evolutionary Algorithms," 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, article no. 5586047, Institute of Electrical and Electronics Engineers, Dec 2010.
The definitive version is available at https://doi.org/10.1109/CEC.2010.5586047
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142446910-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2010