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Metaheuristics for Multiobjective Optimisation

Metaheuristics for Multiobjective Optimisation

Metaheuristics for Multiobjective Optimisation Paperback - 2004 - 2004th Edition

by Gandibleux, Xavier

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Details

  • Title Metaheuristics for Multiobjective Optimisation
  • Author Gandibleux, Xavier
  • Binding Paperback
  • Edition number 2004th
  • Edition 2004
  • Condition New
  • Language ENG
  • Publisher Springer
  • Publication date March 5, 2004
  • Features Bibliography
  • Bookseller's Inventory # ria9783540206378_inp
  • ISBN 9783540206378
  • Quantity available 424

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Reader reviews for Metaheuristics for Multiobjective Optimisation

From the publisher

A large number of real-life optimisation problems can only be realistically modelled with several often conflicting objectives. This fact requires us to abandon the concept of "optimal solution" in favour of vector optimization notions dealing with "efficient solution" and "efficient set". To solve these challenging multiobjective problems, the metaheuristics community has put forward a number of techniques commonly referred to as multiobjective meta- heuristics (MOMH). By its very nature, the field of MOMH covers a large research area both in terms of the types of problems solved and the techniques used to solve these problems. Its theoretical interest and practical applicability have attracted a large number of researchers and generated numerous papers, books and spe- cial issues. Moreover, several conferences and workshops have been organised, often specialising in specific sub-areas such as multiobjective evolutionary op- timisation. The main purpose of this volume is to provide an overview of the current state-of-the-art in the research field of MOMH. This overview is necessar- ily non-exhaustive, and contains both methodological and problem-oriented contributions, and applications of both population-based and neighbourhood- based heuristics. This volume originated from the workshop on multiobjective metaheuristics that was organised at the Carre des Sciences in Paris on November 4-5, 2002. This meeting was a joint effort of two working groups: ED jME and PM20.

First line

The term evolutionary algorithm (EA) stands for a class of stochastic optimization methods that simulate the process of natural evolution.
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