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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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
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Abstract 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]).
AbstractList 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]).
Author Pardalos, Panos M.
Faco, Joao L.
Resende, Mauricio G. C.
Silva, Ricardo M. A.
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  givenname: Mauricio G. C.
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  givenname: Panos M.
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  givenname: Joao L.
  surname: Faco
  fullname: Faco, Joao L.
  email: jldfaco@ufrj.br
  organization: Dept. de Cienc. da Comput., Univ. Fed. do Rio de Janeiro, Rio de Janeiro, Brazil
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Snippet 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...
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StartPage 2201
SubjectTerms Decoding
Genetic algorithms
Linear programming
Optimization
Sociology
Statistics
Vectors
Title Biased random-key genetic algorithm for nonlinearly-constrained global optimization
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