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

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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