This paper describes the application of four evolutionary algorithms to the selection of feature subsets for classification problems. Besides of a simple genetic algorithm (GA), the paper considers three estimation of distribution algorithms (EDAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the EDAs present advantages over th...

Estimation of Distribution Algorithms (EDAs) are evolutionary optimization methods that build models which estimate the distribution of promising regions in the search space. Conventional EDAs use only one single model at a time. One way to efficiently explore multiple areas of the search space is to use multiple models in parallel. In this paper, we present a general framework for both singlea...

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for conti...

Estimation of Distribution Algorithms (EDAs) are implemented mainly by the three steps: selecting the promising subset from the current population, modeling the distribution of the selected population and sampling from the estimated model. Modeling and sampling are key steps of EDAs. They are also research topic of copula theory to represent the multivariate joint distribution by a copula and t...

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural networks with these desired properties. We integrate a DBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We compare the results to the B...

In an effort to improve semiconductor product yields, an engineering data analysis application was developed in 1989 for engineers at Motorola’s Advanced Products Research and Development Laboratory (APRDL). This application was called EDAS – the Engineering Data Analysis System. What began as a small text-based tool providing a handful of statistical reports used in one laboratory has grown to...

The Estimation of Distribution Algorithms are a class of evolutionary algorithms which adopt probabilistic models to reproduce the genetic information of the next generation, instead of conventional crossover and mutation operations. In this paper, we propose new EDAs which incorporate mutation operator to conventional EDAs in order to keep the diversities in EDA populations. Experiments result...

Finding the optimistic triangulation in Bayesian network, is NP hard. Bayesian Optimization Algorithm is a new kind of evolutionary algorithm estimation of distribution algorithms (EDAs). An improved BOA is proposed to get approximate optimistic triangulation in this paper. We carry out four EDAs including our method, on four standard Bayesian networks. Comparing with other Estimation of distri...