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An Application of Erlang Mixture Distributions to modeling the reproduction mechanism in estimation of distribution algorithms

Kołodziej J.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2006

z. 156

203-210

We present the idea of an application of the mixtures of Erlang distributions in the construction of the recombination mechanism in estimation of distribution algorithms. We analyze main properties of Erlang mixtures and define a new Erlang Mixture Estimation of Distribution Algorithm. We try to compare the efficiencies of ErM-EDA and evolutionary strategy in case of large populations. Some experirnental results are presented after simple theoretical studies.

Evolutionary computation based on Bayesian classifiers

Miquelez T., Bengoetxea E., Larranaga P.

International Journal of Applied Mathematics and Computer Science

2004

Vol. 14, no 3

335-349

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.