The job-shop environment has been widely studied under different approaches. It is due to its practical characteristic that makes research interesting. Therefore, the scheduling problem continues being attracted develop new evolutionary algorithms. In this paper, we propose a estimation of distribution algorithm coupled with radial probability function. aforementioned function comes from hydrog...

Abstract. Estimation of Distribution Algorithms (EDAs) are new promising methods in the field of genetic and evolutionary algorithms. In the case of conventional Genetic and Evolutionary Algorithm studies to apply Constraint Satisfaction Problems (CSPs), it is well-known that the incorporation of the domain knowledge in the CSPs is quite effective. In this paper, we propose a hybridization meth...

The Elitist Convergent Estimation of Distribution Algorithm (ECEDA), is a definition of a class of EDA which guarantees convergence to the optimum. This paper introduces the conceptual ECEDA and a practical approach derived from it, called the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). The BUMDA uses a Gaussian model to approximate the Boltzmann distribution, requiring only o...

We survey some randomized search methods for global optimization that are based on sampling from an underlying probability distribution “model” on the solution space. In this approach, the probability model is updated iteratively after evaluating the performance of the samples at each iteration. Such model-based methods include estimation of distribution algorithms (EDAs), the cross-entropy met...

In recent few years expressive progress in the theory and practice of Estimation of Distribution Algorithms (EDA) [1] has appeared, where the classical genetic recombination operators are replaced by probability estimation and stochastic sampling techniques. In this paper we identify some disadvantages of present probabilistic models used in EDAs and propose more general and efficient model for...

This chapter presents the incorporation and use of regular vines into Estimation of Distribution Algorithms for solving numerical optimization problems. Several kinds of statistical dependencies among continuous variables can be taken into account by using regular vines. This work presents a procedure for selecting the most important dependencies in EDAs by truncating regular vines. Moreover, t...

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