Hybrid knowledge-based evolutionary many-objective optimization

Abstract

Knowledge-based optimization is a recent direction in evolutionary optimization research which aims at understanding the optimization process, discovering relationships between decision variables and performance parameters, and using discovered knowledge to improve the optimization process, using machine learning techniques. A novel evolutionary optimization framework that incorporates a knowledge-based representation to search for Pareto optimal patterns in decision space was proposed earlier. This paper extends this framework to problems with four and more objectives, commonly referred to as many-objective optimization problems, using a hybridization approach with NSGA3. Experimental results on standard test functions are presented to demonstrate the advantages of the proposed hybrid algorithm in both objective and decision spaces.