Journal article
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary computation, Vol.8(2), pp.223-247
06/01/2000
DOI: 10.1162/106365600568185
PMID: 10843522
Abstract
Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.
Details
- Title: Subtitle
- Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
- Creators
- Filippo Menczer - University of IowaMelania Degeratu - University of IowaW. Nick Street - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Evolutionary computation, Vol.8(2), pp.223-247
- Publisher
- MIT Press
- DOI
- 10.1162/106365600568185
- PMID
- 10843522
- ISSN
- 1063-6560
- eISSN
- 1530-9304
- Number of pages
- 25
- Language
- English
- Date published
- 06/01/2000
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380453102771
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