Estimation of von mises-fisher distribution algorithm, with application to support vector classification

Ajimakin, AD and Devi, VS (2021) Estimation of von mises-fisher distribution algorithm, with application to support vector classification. In: 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, 10-14 Jul 2021, pp. 199-200.

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Abstract

The most common method in the Evolutionary Algorithm community to handle constraints is to use penalties. The simplest being the death penalty, which rejects solutions that violate constraints. However, its inefficiency in search spaces possessing small feasible regions spurred research into adaptive penalties and other competitive methods. A major criticism of these approaches is that they require the user to fine-tune parameters or design problem-dependent operators. We propose to do away with penalty functions for problems over the Euclidean space when the constraint is an equality concerning the Euclidean distance. This paper describes an evolutionary algorithm on the unit hypersphere based on representing the population with the von Mises-Fisher probability distribution from the field of Directional statistics. We demonstrate its utility by solving the support vector classification problem for a few datasets. © 2021 Owner/Author.

Item Type:	Conference Paper
Publication:	GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
Publisher:	Association for Computing Machinery, Inc
Additional Information:	The copyright for this article belongs to Association for Computing Machinery, Inc
Keywords:	Classification (of information); Population statistics; Probability distributions, Adaptive penalty; Directional statistics; Euclidean distance; Euclidean spaces; Penalty function; Support vector classification; Von mises fishers; Von Mises-Fisher distribution, Evolutionary algorithms
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	20 Nov 2021 11:32
Last Modified:	20 Nov 2021 11:32
URI:	http://eprints.iisc.ac.in/id/eprint/69865

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