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Guiding Single-Objective Optimization Using Multi-objective Methods

  • Mikkel T. Jensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives guiding the search),the average performance of a standard GA can be significantly improved.The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima.The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low.However,a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically.

Keywords

Problem Instance Multiobjective Optimization Traditional Algorithm Travelling Salesperson Problem Good Building Block 
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

  • Mikkel T. Jensen
    • 1
  1. 1.EVALife,Department of Computer ScienceUniversity of AarhusAarhus CDenmark

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