Diagnosing the population state in a genetic algorithm using Hamming distance

Radu Belea, Sergiu Caraman, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

In the literature, the term premature convergence of the entire population is used with the meaning of closing the evolution before reaching the optimal point. It can be emphasized only on a test function with known landscape. If the function landscape is unknown, one can notice the population convergence only. This paper aims to answer to the question: “how can we influence the control parameters of the genetic algorithm so that the exploration time of the parameter space be longer and the risk of premature convergence be reduced?”. The answer to the above question implies choosing a crossover operator with good performances in the landscape exploration and the use of two performance indicators for the detection of the population convergence. In choosing the control parameters of the genetic algorithm, the fitness function landscape must be taken into consideration.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
Place of PublicationBerlin
PublisherSpringer Verlag
Pages246-255
Number of pages10
Volume3215
ISBN (Electronic)978-3-540-30134-9
ISBN (Print)978-3-540-23205-6
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems - Wellington, New Zealand
Duration: 20 Sep 200425 Sep 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems
Abbreviated titleKES 2004
CountryNew Zealand
CityWellington
Period20/09/0425/09/04

Keywords

  • Arithmetic crossover
  • Binary-coded genes
  • Chromosomes
  • Genetic algorithm
  • Hamming distance
  • Real-coded genes
  • Uniform crossover

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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