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

Authors:
Ricardo Martins de Abreu Silva;Mauricio G.C. Resende;Panos M. Pardalos;Joao L. Faco
Affiliations:
Universidade Federal de Pernambuco, Recife-PE, Brazil;AT&T LAbs Research, Florham Park, NJ, USA;University of Florida, Gainsville, FL, USA;Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

Citing 2
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Abstract

In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to linear constraints. Experimental results illustrate its effectiveness on the g01 and g14 problems from CEC2006 benchmark [5].