Comparison of Selection Methods for Evolutionary Optimization

Selection is an essential component of evolutionary algorithms, playing an important role especially in solving hard optimization problems. Most previous studies on selection have focused on more or less ideal properties based on asymptotic analysis. In this paper, we address the selection problem from a more practical point of view by considering solution quality achievable within acceptable time. The repertoire of methods we compare includes proportional selection, ranking selection, linear ranking, tournament, Genitor selection, simulated annealing, and hill-climbing. All these methods use genetic operators in one form or another to create new search points. Experiments are performed in the context of the machine layout design problem. This problem is a real industrial application having both continuous and discrete optimization characteristics. The experimental results for solving two-row machine layout problems of size ranging from 10 to 50 show strong evidence that ranking and tournament selection are, in general, more effective in both solution quality and convergence time than proportional selection and other methods. We provide a theoretical explanation of the experimental results using a predictive model of evolutionary optimization based on selection differential and response to selection.