Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …

We propose a data mining approach for market managers that uses artificial neural networks
(ANNs) guided by genetic algorithms (GAs). Our predictive model allows the selection of an …

Feature selection is a common and key problem in many classification and regression tasks.
It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it …

Feature subset selection is important not only for the insight gained from determining
relevant modeling variables but also for the improved understandability, scalability, and …

Feature subset selection is an important problem in knowledge discovery, not only for the
insight gained from determining relevant modeling variables, but also for the improved …

Robot configuration design is hampered by the lack of established, well-known design rules,
and designers cannot easily grasp the design space and the impact of design variables on …

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 …

This paper discusses the relevance and possible applications of evolutionary algorithms,
particularly genetic algorithms, in the domain of knowledge discovery in databases …

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 …

Attribute selection is an important preprocessing task for the application of a classification
algorithm to a given data set. This task often involves the simultaneous optimization of two or …