Lecture Notes in Computer Science

Genetic and Evolutionary Computation — GECCO 2004

Genetic and Evolutionary Computation Conference Seattle, WA, USA, June 26–30, 2004 Proceedings, Part I

Editors: Deb, K., Poli, R., Banzhaf, W., Beyer, H.-G., Burke, E., Darwen, P., Dasgupta, D., Floreano, D., Foster, J., Harman, M., Holland, O., Lanzi, P.L., Spector, L., Tettamanzi, A.G.B., Thierens, D., Tyrrell, A. (Eds.)

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About this book

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.

Table of contents (165 chapters)

  • PID Controller Tuning for Stable and Unstable Processes Applying GA

    Paz-Ramos, Marco Antonio (et al.)

    Pages 1-10

  • Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

    Pedersen, Gerulf K. M. (et al.)

    Pages 11-23

  • Parameter-Less Hierarchical BOA

    Pelikan, Martin (et al.)

    Pages 24-35

  • Computational Complexity and Simulation of Rare Events of Ising Spin Glasses

    Pelikan, Martin (et al.)

    Pages 36-47

  • Fitness Inheritance in the Bayesian Optimization Algorithm

    Pelikan, Martin (et al.)

    Pages 48-59

Buy this book

eBook $69.99
price for USA (gross)
  • ISBN 978-3-540-24855-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $99.00
price for USA
  • ISBN 978-3-540-22343-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Genetic and Evolutionary Computation — GECCO 2004
Book Subtitle
Genetic and Evolutionary Computation Conference Seattle, WA, USA, June 26–30, 2004 Proceedings, Part I
Editors
  • Kalyanmoy Deb
  • Riccardo Poli
  • Wolfgang Banzhaf
  • Hans-Georg Beyer
  • Edmund Burke
  • Paul Darwen
  • Dipankar Dasgupta
  • Dario Floreano
  • James Foster
  • Mark Harman
  • Owen Holland
  • Pier Luca Lanzi
  • Lee Spector
  • Andrea G. B. Tettamanzi
  • Dirk Thierens
  • Andy Tyrrell
Series Title
Lecture Notes in Computer Science
Series Volume
3103
Copyright
2004
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-24855-2
DOI
10.1007/b98645
Softcover ISBN
978-3-540-22343-6
Series ISSN
0302-9743
Edition Number
1
Number of Pages
C, 1448
Number of Illustrations and Tables
660 b/w illustrations
Topics