Biased random-key genetic algorithm for bound-constrained global optimization
R. M. A. Silva, Maurício G. C. Resende, Pãnos M. Pardalos, José Fernando Gonçalves · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 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 [7].