Multiagent visual area coverage using a new genetic algorithm selection scheme

Using genetic algorithms (GA) for solving NP-hard problems is becoming more and more frequent. This paper presents a use of GA with a new selection approach called the queen GA. The main idea is not to select both parents from the entire population, but to create a subgroup of better solutions (the...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 175; no. 3; pp. 1890 - 1907
Main Authors Stern, Helman, Chassidim, Yoash, Zofi, Moshe
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 16.12.2006
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Covering problems
Genetic algorithm
Genetic algorithms
Multiagent
Scheduling
Scheduling algorithms
Selection
Studies
Visual area
Visual search
Covering problems
Scheduling
Visual search
Genetic algorithm
Visual area
Multiagent
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ISSN0377-2217
1872-6860
DOI10.1016/j.ejor.2005.02.078

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Summary:Using genetic algorithms (GA) for solving NP-hard problems is becoming more and more frequent. This paper presents a use of GA with a new selection approach called the queen GA. The main idea is not to select both parents from the entire population, but to create a subgroup of better solutions (the queen cohort), and to use at least one of its members in each performed crossover. We demonstrate the use of the queen GA for the problem of repositioning observers across a polygonal area with obstacles in order to maximize the visual area coverage for a given time horizon. The queen GA gives superior results over a GA with different selection methods (i.e. proportion, ranking and tournament) at the 0.01 significance level. These comparative results were duplicated when elitism was included.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.02.078