The Allele Meta-model - Developing a common language for Genetic Algorithms

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Abstract

Due to the lot of different Genetic Algorithm variants, encodings, and attacked problems, very little general theory is available to explain the internal functioning of Genetic Algorithms. Consequently it is very difficult for researchers to find a common language to document quality improvements of newly developed algorithms. In this paper the authors present a new Allele Meta-Model enabling a problem-independent description of the search process inside Genetic Algorithms. Based upon this meta-model new measurement values are introduced that can be used to measure genetic diversity, genetic flexibility, or optimization potential of an algorithm's population. On the one hand these values help Genetic Algorithm researchers to understand algorithms better and to illustrate newly developed techniques more clearly. On the other hand they are also meaningful for any GA user e.g. to tune parameters or to identify performance problems.

Original languageEnglish
Pages (from-to)202-211
Number of pages10
JournalLecture Notes in Computer Science
Volume3562
Issue numberPART II
DOIs
Publication statusPublished - 2005
EventFirst International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005 - Las Palmas, Canary Islands, Spain
Duration: 15 Jun 200518 Jun 2005

Keywords

  • Genetic Algorithms

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