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Introduction to Evolutionary Algorithms

  • Rudolf Kruse
  • Christian Borgelt
  • Frank Klawonn
  • Christian Moewes
  • Matthias Steinbrecher
  • Pascal Held
Part of the Texts in Computer Science book series (TCS)

Abstract

Evolutionary algorithms comprise a class of optimization techniques that imitate principles of biological evolution. They belong to the family of metaheuristics, which also includes, for example, particle swarm and ant colony optimization, which are inspired by other biological structures and processes, as well as classical methods like simulated annealing, which is inspired by a thermodynamical process. The core principle of evolutionary algorithms is to apply evolution principles like mutation and selection to populations of candidate solutions in order to find a (sufficiently good) solution for a given optimization problem.

Keywords

Search Space Simulated Annealing Evolutionary Algorithm Solution Candidate Biological Evolution 
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 London 2013

Authors and Affiliations

  • Rudolf Kruse
    • 1
  • Christian Borgelt
    • 2
  • Frank Klawonn
    • 3
  • Christian Moewes
    • 1
  • Matthias Steinbrecher
    • 4
  • Pascal Held
    • 1
  1. 1.Faculty of Computer ScienceOtto-von-Guericke University MagdeburgMagdeburgGermany
  2. 2.Intelligent Data Analysis & Graphical Models Research UnitEuropean Centre for Soft ComputingMieresSpain
  3. 3.FB InformatikOstfalia University of Applied SciencesWolfenbüttelGermany
  4. 4.SAP Innovation CenterPotsdamGermany

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