Biased random-key genetic algorithm for nonlinearly-constrained global optimization

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to nonlinear constrain...

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Bibliographic Details
Published in2013 IEEE Congress on Evolutionary Computation pp. 2201 - 2206
Main Authors Silva, Ricardo M. A., Resende, Mauricio G. C., Pardalos, Panos M., Faco, Joao L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Decoding
Genetic algorithms
Linear programming
Optimization
Sociology
Statistics
Vectors
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Summary:Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to nonlinear constraints. Experimental results illustrate its effectiveness on some functions from CEC2006 benchmark (Liang et al. [2006]).
ISBN:1479904538
9781479904532
ISSN:1089-778X
DOI:10.1109/CEC.2013.6557830