DocumentCode
618079
Title
Biased random-key genetic algorithm for nonlinearly-constrained global optimization
Author
Silva, Ricardo M. A. ; Resende, M.G.C. ; Pardalos, Panos M. ; Faco, Joao L.
Author_Institution
Centro de Inf., Univ. Fed. de Pernambuco, Recife, Brazil
fYear
2013
fDate
20-23 June 2013
Firstpage
2201
Lastpage
2206
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]).
Keywords
genetic algorithms; nonlinear programming; biased random-key genetic algorithm; bound-constrained continuous global optimization problems; continuous domain; discrete domain; multimodal function; nonlinearly-constrained global optimization; Decoding; Genetic algorithms; Linear programming; Optimization; Sociology; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location
Cancun
Print_ISBN
978-1-4799-0453-2
Electronic_ISBN
978-1-4799-0452-5
Type
conf
DOI
10.1109/CEC.2013.6557830
Filename
6557830
Link To Document