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.
This work is funded as part of the European Commission Information Society Technologies Programme (Future and Emerging Technologies). The authors have sole responsibility for this work, it does not represent the opinion of the European Community, and the European Community is not responsible for any use that may be made of the data appearing herein.
Also in RGAI, MTA-SZTE, Szeged, Hungary
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Jelasity, M., Preuβ, M., Eiben, A.E. (2002). Operator Learning for a Problem Class in a Distributed Peer-to-Peer Environment. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_17
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DOI: https://doi.org/10.1007/3-540-45712-7_17
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