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Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms

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Soft Computing: Methodologies and Applications

Part of the book series: Advances in Soft Computing ((AINSC,volume 32))

Summary

In this paper, we propose a replacement strategy for steady-state genetic algorithms that takes into account two features of the element to be included into the population: a measure of the contribution of diversity to the population and the fitness function. In particular, the proposal attempts to replace an element in the population with worse values for these two features. In this way, the diversity of the population is increased and the quality of its solutions is improved, simultaneously, maintaining high levels of useful diversity. Experimental results show that the use of the proposed replacement strategy allows significant performance to be achieved for problems with different difficulties, which regards to other replacement strategies presented in the literature.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Manuel Lozano & Francisco Herrera

  2. Dept. of Electronic Engineering, Computer Systems and Automatics, Escuela Superior de La Rábida, University of Huelva, 21819, Huelva, Spain

    José Ramón Cano

Authors
  1. Manuel Lozano
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  2. Francisco Herrera
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  3. José Ramón Cano
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Editor information

Editors and Affiliations

  1. Fakultät für Elektrotechnik und Informationstechnik, Lehrstuhl für Elektrische Steuerung und Regelung, Universität Dortmund, Otto-Hahn-Str. 4, 44227, Dortmund, Germany

    Frank Hoffmann

  2. Fraunhofer IPK, Berlin

    Mario Köppen

  3. Institut für Sicherheits-/Prüftechnik, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  4. FH Braunschweig/Wolfenbüttel, Salzdahlumer Str. 46/48, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Enterprise Integration, Cranfield University, Cranfield, Bedford, MK43 0AL, UK

    Rajkumar Roy

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© 2005 Springer-Verlag Berlin Heidelberg

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Lozano, M., Herrera, F., Cano, J.R. (2005). Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds) Soft Computing: Methodologies and Applications. Advances in Soft Computing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32400-3_7

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  • DOI: https://doi.org/10.1007/3-540-32400-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25726-4

  • Online ISBN: 978-3-540-32400-3

  • eBook Packages: EngineeringEngineering (R0)

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