Diversity-based offspring selection criteria for genetic algorithms

Andreas Scheibenpflug, Stefan Wagner, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
EditorsFranz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Number of pages8
ISBN (Print)9783319273396
Publication statusPublished - 2015
Event15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain
Duration: 8 Feb 201513 Feb 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Computer Aided Systems Theory, Eurocast 2015
CityLas Palmas, Gran Canaria
Internet address


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