Towards automatic domain knowledge extraction for evolutionary heuristics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Domain knowledge is essential for successful problem solving and optimization. This paper introduces a framework in which a form of automatic domain knowledge extraction can be implemented using concepts from the field of machine learning. The result is an encoding of the type used in most evolutionary computation (EC) algorithms. The approach focuses on whole problem domains instead of single problems. After the theoretical validation of the algorithm the main idea is given impetus by showing that on different subdomains of linear functions the method finds different encodings which result in different problem complexities.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature PPSN VI - 6th International Conference, Proceedings
EditorsKalyanmoy Deb, Xin Yao, Gunther Rudolph, Hans-Paul Schwefel, Juan Julian Merelo, Evelyne Lutton, Marc Schoenauer
PublisherSpringer Verlag
Pages755-764
Number of pages10
ISBN (Print)9783540410560
Publication statusPublished - Jan 1 2000
Event6th International Conference on Parallel Problem Solving from Nature, PPSN 2000 - Paris, France
Duration: Sep 18 2000Sep 20 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1917
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Parallel Problem Solving from Nature, PPSN 2000
CountryFrance
CityParis
Period9/18/009/20/00

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Jelasity, M. (2000). Towards automatic domain knowledge extraction for evolutionary heuristics. In K. Deb, X. Yao, G. Rudolph, H-P. Schwefel, J. J. Merelo, E. Lutton, & M. Schoenauer (Eds.), Parallel Problem Solving from Nature PPSN VI - 6th International Conference, Proceedings (pp. 755-764). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1917). Springer Verlag.