Diversity-based offspring selection criteria for genetic algorithms

Andreas Scheibenpflug; Stefan Wagner; Michael Affenzeller

doi:10.1007/978-3-319-27340-2_49

Diversity-based offspring selection criteria for genetic algorithms

Andreas Scheibenpflug, Stefan Wagner, Michael Affenzeller

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

Abstract

Genetic algorithms can be affected by an early loss of diversity in their populations called premature convergence. To address this problem, this paper presents two extensions for the offspring selection genetic algorithm. Both extensions are based on diversity maintenance mechanisms applied when selecting offspring for the next generation. The first approach focuses on producing solutions that feature a predefined quality improvement as well as an appropriate structural distance from their parents. The second approach monitors the average diversity of the population and selects more diverse offspring if the population does not meet a predefined diversity. We show that these algorithms allow to control diversity and are useful methods for influencing the development of the population independent of the algorithms other parameters.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Redakteure/-innen	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Herausgeber (Verlag)	Springer
Seiten	393-400
Seitenumfang	8
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_49
Publikationsstatus	Veröffentlicht - 2015
Veranstaltung	15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spanien Dauer: 8 Feb. 2015 → 13 Feb. 2015 http://eurocast2015.fulp.ulpgc.es/

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	9520
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	15th International Conference on Computer Aided Systems Theory, Eurocast 2015
Land/Gebiet	Spanien
Ort	Las Palmas, Gran Canaria
Zeitraum	08.02.2015 → 13.02.2015
Internetadresse	http://eurocast2015.fulp.ulpgc.es/

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10.1007/978-3-319-27340-2_49

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Scheibenpflug, A., Wagner, S., & Affenzeller, M. (2015). Diversity-based offspring selection criteria for genetic algorithms. in F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Hrsg.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (S. 393-400). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520). Springer. https://doi.org/10.1007/978-3-319-27340-2_49

Scheibenpflug, Andreas ; Wagner, Stefan ; Affenzeller, Michael. / Diversity-based offspring selection criteria for genetic algorithms. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer, 2015. S. 393-400 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Genetic algorithms can be affected by an early loss of diversity in their populations called premature convergence. To address this problem, this paper presents two extensions for the offspring selection genetic algorithm. Both extensions are based on diversity maintenance mechanisms applied when selecting offspring for the next generation. The first approach focuses on producing solutions that feature a predefined quality improvement as well as an appropriate structural distance from their parents. The second approach monitors the average diversity of the population and selects more diverse offspring if the population does not meet a predefined diversity. We show that these algorithms allow to control diversity and are useful methods for influencing the development of the population independent of the algorithms other parameters.",

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Scheibenpflug, A, Wagner, S & Affenzeller, M 2015, Diversity-based offspring selection criteria for genetic algorithms. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (Hrsg.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 9520, Springer, S. 393-400, 15th International Conference on Computer Aided Systems Theory, Eurocast 2015, Las Palmas, Gran Canaria, Spanien, 08.02.2015. https://doi.org/10.1007/978-3-319-27340-2_49

Diversity-based offspring selection criteria for genetic algorithms. / Scheibenpflug, Andreas; Wagner, Stefan ; Affenzeller, Michael.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Hrsg. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer, 2015. S. 393-400 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9520).

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

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Y1 - 2015

N2 - Genetic algorithms can be affected by an early loss of diversity in their populations called premature convergence. To address this problem, this paper presents two extensions for the offspring selection genetic algorithm. Both extensions are based on diversity maintenance mechanisms applied when selecting offspring for the next generation. The first approach focuses on producing solutions that feature a predefined quality improvement as well as an appropriate structural distance from their parents. The second approach monitors the average diversity of the population and selects more diverse offspring if the population does not meet a predefined diversity. We show that these algorithms allow to control diversity and are useful methods for influencing the development of the population independent of the algorithms other parameters.

AB - Genetic algorithms can be affected by an early loss of diversity in their populations called premature convergence. To address this problem, this paper presents two extensions for the offspring selection genetic algorithm. Both extensions are based on diversity maintenance mechanisms applied when selecting offspring for the next generation. The first approach focuses on producing solutions that feature a predefined quality improvement as well as an appropriate structural distance from their parents. The second approach monitors the average diversity of the population and selects more diverse offspring if the population does not meet a predefined diversity. We show that these algorithms allow to control diversity and are useful methods for influencing the development of the population independent of the algorithms other parameters.

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Scheibenpflug A, Wagner S , Affenzeller M. Diversity-based offspring selection criteria for genetic algorithms. in Pichler F, Moreno-Díaz R, Quesada-Arencibia A, Hrsg., Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer. 2015. S. 393-400. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_49

Diversity-based offspring selection criteria for genetic algorithms

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