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 in:2013 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
Language:English
Published: IEEE 01.06.2013
Subjects:
Decoding
Genetic algorithms
Linear programming
Optimization
Sociology
Statistics
Vectors
ISBN:1479904538, 9781479904532
ISSN:1089-778X
Online Access:Get full text
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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