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Effective Use of Directional Information in Multi-objective Evolutionary Computation

  • Martin Brown
  • Robert E. Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

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 steepest-descent, 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 multi-objective 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.

Keywords

Search Direction Pareto Front Descent Direction Directional Cone Evolutionary Computation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Martin Brown
    • 1
  • Robert E. Smith
    • 2
  1. 1.Department of Computing and MathematicsManchester Metropolitan UniversityManchesterUK
  2. 2.The Intelligent Computer Systems CentreThe University of The West of EnglandBristolUK

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