Parent to Mean-Centric Self-Adaptation in SBX Operator for Real-Parameter Optimization

Abstract

Most real-parameter genetic algorithms (RGAs) use a blending of participating parent solutions to create offspring solutions in its recombination operator. The blending operation creates solutions either around one of the parent solutions (having a parent-centric approach) or around the centroid of the parent solutions (having a mean-centric approach). In this paper, we argue that a self-adaptive approach in which a parent or a mean-centric approach is adopted based on population statistics is a better procedure than either approach alone. We propose a self-adaptive simulated binary crossover (SA-SBX) approach for this purpose. On a test suite of six unimodal and multi-modal test problems, we demonstrate that a RGA with SA-SBX approach performs consistently better in locating the global optimum solution than RGA with original SBX operator and RGA with mean-centric SBX operator.