A Study of the Combination of Variation Operators in the NSGA-II Algorithm View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Antonio J. Nebro , Juan J. Durillo , Mirialys Machín , Carlos A. Coello Coello , Bernabé Dorronsoro

ABSTRACT

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior. This way, we explore the combined use of three different operators (simulated binary crossover, differential evolution’s operator, and polynomial mutation) in the NSGA-II algorithm. We have considered two strategies for selecting the operators: random and adaptive. The resulting variants have been tested on a set of 19 complex problems, and our results indicate that both schemes significantly improve the performance of the original NSGA-II algorithm, achieving the random and adaptive variants the best overall results in the bi- and three-objective considered problems, respectively. More... »

PAGES

269-278

References to SciGraph publications

Book

TITLE

Advances in Artificial Intelligence

ISBN

978-3-642-40642-3
978-3-642-40643-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-40643-0_28

DOI

http://dx.doi.org/10.1007/978-3-642-40643-0_28

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046837565


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