Systematic integration of parameterized local search into evolutionary algorithms

Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into...

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Bibliographic Details
Published inIEEE transactions on evolutionary computation Vol. 8; no. 2; pp. 137 - 155
Main Authors Bambha, N.K., Bhattacharyya, S.S., Teich, J., Zitzler, E.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.04.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Applied sciences
Artificial intelligence
Computer science
Computer science; control theory; systems
Computer simulation
Constraint optimization
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Genetics
Heating
Laboratories
Learning and adaptive systems
Military computing
Optimization
Optimization methods
Process design
Searching
Space technology
Strategy
Evolutionary algorithm (EA)
hybrid global/local search
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Summary:Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into evolutionary algorithms (EAs). Using the framework of simulated heating, we investigate both static and dynamic strategies for systematically managing the tradeoff between PLSA accuracy and optimization effort. Our goal is to achieve maximum solution quality within a fixed optimization time budget. We show that the simulated heating technique better utilizes the given optimization time resources than standard hybrid methods that employ fixed parameters, and that the technique is less sensitive to these parameter settings. We apply this framework to three different optimization problems, compare our results to the standard hybrid methods, and show quantitatively that careful management of this tradeoff is necessary to achieve the full potential of an EA/PLSA combination.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2004.823471