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Diversity-Based Offspring Selection Criteria for Genetic Algorithms

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 9520)

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.

Keywords

Offspring Survival Genetic Algorithm (GA) Success Ratio Diversity Charter High Quality Difference 
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 International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Heuristic and Evolutionary Algorithms Laboratory, School of InformaticsCommunications and Media University of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler University LinzLinzAustria

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