Biased random-key genetic algorithm for nonlinearly-constrained global optimization
Ricardo M. A. Silva, Maurício G. C. Resende, Pãnos M. Pardalos, João Lauro D. Facó · 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]).