In many modeling problems that are based on input–output data, information about a
plethora of variables is available. In these cases, the proper selection of explanatory …

Abstract Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of Darwinian evolution. The motivation for applying EAs to Data Mining is that they …

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical
weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated …

Feature selection for ensembles has shown to be an effective strategy for ensemble creation
due to its ability of producing good subsets of features, which make the classifiers of the …

The development of powerful computers and faster input/output devices coupled with the
need for storing and analyzing data have resulted in massive databases (of the order of …

We examine three methods for improving the ability of evolutionary multiobjective
optimization (EMO) algorithms to find a variety of fuzzy rule-based classification systems with …

This paper presents a methodology to generate representations for isolated handwritten
symbols, modeled as a multi-objective optimization problem. We detail the methodology …

Real-world datasets tend to be complex, large in size, and may contain many irrelevant
features. Eliminating such irrelevant features can significantly improve the performance of a …

The aim of this study is to develop a feature subset selection (FSS) method based on the
margin of support vector machines (SVM). The problem of directly using the SVM margin is …

The performance of genetic algorithms (GAs) is dependent on many factors. In this paper,
we have isolated one factor: the crossover operator. Commonly used crossover operators …