Evolutionary Computation

  • Roberto Baragona
  • Francesco Battaglia
  • Irene Poli
Chapter
First Online:
Part of the Statistics and Computing book series (SCO)

Abstract

The evolutionary computation methods are introduced by discussing their origin inside the artificial intelligence framework, and the contributions of Darwin’s theory of natural evolution and Genetics. We attempt to highlight the main features of an evolutionary computation method, and describe briefly some of them: evolutionary programming, evolution strategies, genetic algorithm, estimation of distribution algorithms, differential evolution. The remainder of the chapter is devoted to a closer illustration of genetic algorithms and more recent advancements, to the problem of convergence and to the practical use of them. A final section on the relationship between genetic algorithms and random sampling techniques is included.

Keywords

Genetic Algorithm Particle Swarm Optimization Fitness Function Differential Evolution Initial Population 
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 Berlin Heidelberg 2011

Authors and Affiliations

  • Roberto Baragona
    • 1
  • Francesco Battaglia
    • 2
  • Irene Poli
    • 3
  1. 1.Department of Communication and Social ResearchSapienza University of RomeRomeItaly
  2. 2.Department of Statistical SciencesSapienza University of RomeRomaItaly
  3. 3.Department of StatisticsCa’ Foscari University of VeniceVeniceItaly

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