Genetic and Evolutionary Computation – GECCO 2004

Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part II

  • Kalyanmoy Deb
Conference proceedings GECCO 2004

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3103)

Table of contents

  1. Front Matter
  2. Genetic Algorithms (Continued)

    1. Marco Antonio Paz-Ramos, Jose Torres-Jimenez, Enrique Quintero-Marmol-Marquez, Hugo Estrada-Esquivel
      Pages 1-10
    2. Gerulf K. M. Pedersen, David E. Goldberg
      Pages 11-23
    3. Martin Pelikan, Tz-Kai Lin
      Pages 24-35
    4. Martin Pelikan, Jiri Ocenasek, Simon Trebst, Matthias Troyer, Fabien Alet
      Pages 36-47
    5. Martin Pelikan, Kumara Sastry
      Pages 48-59
    6. Joseph Reisinger, Kenneth O. Stanley, Risto Miikkulainen
      Pages 69-81
    7. Mark A. Renslow, Brenda Hinkemeyer, Bryant A. Julstrom
      Pages 82-89
    8. Eduardo Rodriguez-Tello, Jose Torres-Jimenez
      Pages 102-113
    9. Kumara Sastry, David E. Goldberg
      Pages 126-137
    10. Dong-Il Seo, Sung-Soon Choi, Byung-Ro Moon
      Pages 150-161
    11. Weiguo Sheng, Allan Tucker, Xiaohui Liu
      Pages 162-173
    12. Hal Stringer, Annie S. Wu
      Pages 198-209

Other volumes

  1. Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part I
  2. Genetic and Evolutionary Computation – GECCO 2004
    Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part II

About these proceedings

Introduction

MostMOEAsuseadistancemetricorothercrowdingmethodinobjectivespaceinorder to maintain diversity for the non-dominated solutions on the Pareto optimal front. By ensuring diversity among the non-dominated solutions, it is possible to choose from a variety of solutions when attempting to solve a speci?c problem at hand. Supposewehavetwoobjectivefunctionsf (x)andf (x).Inthiscasewecande?ne 1 2 thedistancemetricastheEuclideandistanceinobjectivespacebetweentwoneighboring individuals and we thus obtain a distance given by 2 2 2 d (x ,x )=[f (x )?f (x )] +[f (x )?f (x )] . (1) 1 2 1 1 1 2 2 1 2 2 f wherex andx are two distinct individuals that are neighboring in objective space. If 1 2 2 2 the functions are badly scaled, e.g.[?f (x)] [?f (x)] , the distance metric can be 1 2 approximated to 2 2 d (x ,x )? [f (x )?f (x )] . (2) 1 2 1 1 1 2 f Insomecasesthisapproximationwillresultinanacceptablespreadofsolutionsalong the Pareto front, especially for small gradual slope changes as shown in the illustrated example in Fig. 1. 1.0 0.8 0.6 0.4 0.2 0 0 20 40 60 80 100 f 1 Fig.1.Forfrontswithsmallgradualslopechangesanacceptabledistributioncanbeobtainedeven if one of the objectives (in this casef ) is neglected from the distance calculations. 2 As can be seen in the ?gure, the distances marked by the arrows are not equal, but the solutions can still be seen to cover the front relatively well.

Keywords

Hardware algorithms genetic algorithms genetic programming learning optimization programming robot robotics

Editors and affiliations

  • Kalyanmoy Deb
    • 1
  1. 1.Indian Institute of Technology Kanpur, Department of Mechanical EngineeringKanpur Genetic Algorithms Laboratory (KanGAL)208016India

Bibliographic information

  • DOI https://doi.org/10.1007/b98645
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-22343-6
  • Online ISBN 978-3-540-24855-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book
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