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

R. M. A. Silva. M. G. C. Resende, P.M. Pardalos, and J. F. Gonçalves

Proeedings of Global Optimization Workshop (GOW 2012), D. Aloise, P. Hansen, and C. Rocha (Eds.), pp. 133-136, Natal, Brazil, 2012

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 continuous global optimization problems subject to box constraints. Experimental results illustrate its effectiveness on the robot kinematics problem, a challenging problem according to Floudas et al. (1999).


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