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)


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.


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