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

R.M.A. Silva, M.G.C. Resende, P.M. Pardalos, and J.L.D. Facó

Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2201-2206, Cancun, June 20-23, 2013

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).


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