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Network modelling

Is Selection Optimal for Scale-Free Small Worlds?

Palotai Z.a · Farkas C.b · Lorincz A.a

Author affiliations

aDepartment of Information Systems, Eötvös Loránd University, Budapest, Hungary; bDepartment of Computer Science and Engineering, University of South Carolina, Columbia, S.C., USA

Corresponding Author

A. Lörincz

Department of Information Systems, Eötvös Loránd University

Pázmány Péter sétány 1/c, HU–1117 Budapest (Hungary)

Tel. +36 1 209 0555/8473, Fax +36 1 381 2140, E-Mail andras.lorincz@elte.hu

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The ‘no free lunch theorem’ claims that for the set of all problems no algorithm performs better than random search and, thus, selection can be advantageous only on a limited set of problems. In this paper we investigate how the topological structure of the environment influences algorithmic efficiency. We study the performance of algorithms, using selective learning, reinforcement learning, and their combinations, in random, scale-free, and scale-free small world (SFSW) environments. The learning problem is to search for novel, not-yet-found information. We ran our experiments on a large news site and on its downloaded portion. Controlled experiments were performed on this downloaded portion: we modified the topology, but preserved the publication time of the news. Our empirical results show that the selective learning is the most efficient in SFSW topology. In non-small world topologies, however, the combination of the selective and reinforcement learning algorithms performs the best.

© 2006 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Network modelling

Published online: September 01, 2006
Issue release date: August 2006

Number of Print Pages: 11
Number of Figures: 3
Number of Tables: 2

ISSN: 1424-8492 (Print)
eISSN: 1424-8506 (Online)

For additional information: http://www.karger.com/CPU

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Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
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