Operator learning for a problem class in a distributed peer-to-peer environment

Márk Jelasity, Mike Preuß, A. E. Eiben

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

6 Citations (Scopus)

Abstract

This paper discusses a promising new research direction, the automatic learning of algorithm components for problem classes. We focus on the methodology of this research direction. As an illustration, a mutation operator for a special class of subset sum problem instances is learned. The most important methodological issue is the emphasis on the generalisability of the results. Not only a methodology but also a tool is proposed. This tool is called DRM (distributed resource machine), developed as part of the DREAM project, and is capable of running distributed experiments on the Internet making a huge amount of resources available to the researcher in a robust manner. It is argued that the DRM is ideally suited for algorithm learning.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature - PPSN 2002 - 7th International Conference, Proceedings
EditorsJuan Julian Merelo Guervos, Panagiotis Adamidis, Hans-Georg Beyer, Hans-Paul Schwefel, Jose-Luis Fernandez-Villacanas
PublisherSpringer Verlag
Pages172-183
Number of pages12
ISBN (Print)3540441395
DOIs
Publication statusPublished - Jan 1 2002
Event7th International Conference on Parallel Problem Solving from Nature, PPSN 2002 - Granada, Spain
Duration: Sep 7 2002Sep 11 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2439
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Parallel Problem Solving from Nature, PPSN 2002
CountrySpain
CityGranada
Period9/7/029/11/02

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Jelasity, M., Preuß, M., & Eiben, A. E. (2002). Operator learning for a problem class in a distributed peer-to-peer environment. In J. J. M. Guervos, P. Adamidis, H-G. Beyer, H-P. Schwefel, & J-L. Fernandez-Villacanas (Eds.), Parallel Problem Solving from Nature - PPSN 2002 - 7th International Conference, Proceedings (pp. 172-183). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2439). Springer Verlag. https://doi.org/10.1007/3-540-45712-7_17