Effective Use of Directional Information in Multi-objective Evolutionary Computation

While genetically inspired approaches to multi-objective optimization have many advantages over conventional approaches, they do not explicitly exploit directional/gradient information. This paper describes how steepestdescent, multi-objective optimization theory can be combined with EC concepts to produce improved algorithms. It shows how approximate directional information can be efficiently extracted from parent individuals, and how a multiobjective gradient can be calculated, such that children individuals can be placed in appropriate, dominating search directions. The paper describes and introduces the basic theoretical concepts as well as demonstrating some of the concepts on a simple test problem.