Genetic Algorithms perform crossovers effectively when we can identify a set of loci tightly linked to form a building block. Several methods have been proposed to detect such linkage. Linkage identification methods investigate fitness differences by perturbations of gene values and EDAs estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of...

In this paper we address the problem of model selection in Estimation of Distribution Algorithms from a novel perspective. We perform an implicit model selection by transforming the variables and choosing a low dimensional model in the new variable space. We apply such paradigm in EDAs and we introduce a novel algorithm called I-FCA, which makes use of the independence model in the transformed ...

Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation–of– Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problem’s structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probab...

Estimation of distribution algorithms (EDAs) have become promising kinds of evolutionary algorithms. They are based on sampling an estimated distribution of the better of the solutions from the last generation – iteration of the algorithm. Learning the distribution and sampling is used instead of reproduction common in traditional evolutionary algorithms. In many real-world optimization tasks, ...

One of the important aspects of online shopping is the availability of electronic decision aids (EDAs). The use of these increasingly pervasive shopping tools may affect key relationships underlying consumers’ search and processing with respect to product related information. The primary objective of this research is to investigate the effects of electronic decision aids on consumers’ product e...

Numerical simulations are conducted using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to investigate the impact of land–vegetation processes on the prediction of mesoscale convection observed on 24–25 May 2002 during the International H2O Project (IHOP_2002). The control COAMPS configuration uses the Weather Research and Forecasting (WRF) model version of the Noah land sur...

Multimemetic algorithms (MMAs) are memetic algorithms in which memes (interpreted as non-genetic expressions of problemsolving strategies) are explicitly represented and evolved alongside genotypes. This process is commonly approached using the standard genetic procedures of recombination and mutation to manipulate directly information at the memetic level. We consider an alternative approach b...

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear; 2) the likelihood surface has a huge number of peaks; 3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ...

TheWCORmethodology makes use of metaheuristic algorithms to find the best set of rules, as well as their weights, when learning weighted linguistic fuzzy systems from data. Although in early work based on this approach the search was carried out by means of a genetic algorithm, any other technique can be used. Estimation of distribution algorithms (EDAs) are a family of evolutionary algorithms ...

Finding global optima in the continuous domain is challenging for Genetic Algorithms (GAs). Traditional GAs use either binary-coded or real-coded representation of the variables, but there is a trade-off between these two encoding methods. Recombination operators for binary-coded GAs are simple to design, but the length of the string representing an individual would be huge if the number of des...