This paper combines feature selection methods with a two-stage evolutionary classifier
based on product unit neural networks. The enhanced methodology has been tried out with …

This paper presents a procedure to add broader diversity at the beginning of the
evolutionary process. It consists of creating two initial populations with different parameter …

This paper presents a workbench to get simple neural classification models based on
product evolutionary networks via a prior data preparation at attribute level by means of filter …

This paper proposes a methodology to feature sorting in the context of supervised machine
learning algorithms. Feature sorting is defined as a procedure to order the initial …

This paper develops three frameworks based on a metaheuristic algorithm to train neural
network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first …

INTELIGENCIA ARTIFICIAL New training approaches for classification based on evolutionary
neural networks. Application to product Page 1 Inteligencia Artificial 17(54), 30-34 doi …

This chapter considers a recent algorithm to add broader diversity at the beginning of the
evolutionary process and extends it to sigmoidal neural networks. A simultaneous evolution …

This thesis introduces three contributions to train feed-forward neural network models based
on evolutionary computation for a classification task. The new methodologies have been …

This paper proposes a methodology to feature sorting in the context of supervised machine
learning algorithms. Feature sorting is defined as a procedure to order the initial …