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
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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