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Information theoretic justification of Boltzmann selection and its generalization to Tsallis case

Dukkipati, Ambedkar and Murty, Narasimha M and Bhatnagar, Shalabh (2005) Information theoretic justification of Boltzmann selection and its generalization to Tsallis case. In: IEEE Congress on Evolutionary Computation, 2-5 Sept, 2005, Monterey, CA, United States, pp. 1667-1674.

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Abstract

A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of Boltzmann distribution, to weigh the configurations in the selection mechanism. This generalization is motivated by the recently proposed generalized simulated annealing algorithm based on Tsallis statistics. We also present an information theoretic justification to use Boltzmann distribution in the selection mechanism, since these 'canonical' distributions have deep roots in information theory. Our simulation results show that for an appropriate choice of nonextensive index that is offered by Tsallis statistics, evolutionary algorithms based on this generalization outperform algorithms based on Boltzmann distribution.

Item Type: Conference Paper
Additional Information: ©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 23 Feb 2008
Last Modified: 19 Sep 2010 04:42
URI: http://eprints.iisc.ernet.in/id/eprint/13078

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