Combinatorial optimization EDA using hidden Markov models
Marc-André Gardner, Christian Gagné, Marc Parizeau · 2013
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, although they are a very powerful tool for estimating sequential samples. This paper thus proposes a new method, called HMM-EDA, implementing this idea, along with some preliminary experimental results.