Operator Learning for a Problem Class in a Distributed Peer-to-Peer Environment

  • Márk Jelasity
  • Mike Preuβ
  • A. E. Eiben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Márk Jelasity
    • 1
  • Mike Preuβ
    • 2
  • A. E. Eiben
    • 1
  1. 1.Free University of AmsterdamAmsterdamThe Netherlands
  2. 2.University of DortmundDortmundGermany

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