Biased random-key genetic algorithm for nonlinearly-constrained global optimization - Silva, Ricardo M. A.; Resende, Mauricio G. C.; Pardalos, Panos M.; Faco, Joao L. (2013 IEEE Congress on Evolutionary Computation, 2013)
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]).