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Estimation of Distribution Algorithm based on Hidden Markov Models for Combinatorial Optimization

Marc-André Gardner, Christian Gagné and Marc Parizeau

Abstract - Estimation of Distribution Algorithms (EDAs) have been successfully applied to a wide variety of problems. The algorithmic model of EDA is generic and can virtually be used with any distribution model, ranging from the mere Bernoulli distribution to the sophisticated Bayesian network. The Hidden Markov Model (HMM) is a well-known graphical model useful for modelling populations of variable-length sequences of discrete values. Surprisingly, HMMs have not yet been used as distribution estimators for an EDA, even though it is a very powerful tool especially designed for modelling sequences. We thus propose a new method, called HMM-EDA, implementing this idea. Preliminary comparative results on two classical combinatorial optimization problems show that HMM-EDA is indeed a promising approach for problems that have sequential representations.

Bibtex:

@inproceedings{Gardner975,
author    = { Marc-André Gardner and Christian Gagné and Marc Parizeau },
title     = { Estimation of Distribution Algorithm based on Hidden Markov Models for Combinatorial Optimization },
booktitle = { Companion proc. of the Genetic and Evolutionary Computation Conference (GECCO 2013) },
year      = { 2013 },
month     = { July 6-10 },
location  = { Amsterdam, The Netherlands }
}

Last modification: 2013/05/21 by cgagne

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