Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions
in a single simulation run has practical relevance to problem solving across many fields …

Heart diseases are of notable public health disquiet worldwide. Heart patients are growing
speedily owing to deficient health awareness and bad consumption lifestyles. Therefore, it is …

Learning and evolution are two fundamental forms of adaptation. There has been a great
interest in combining learning and evolution with artificial neural networks (ANNs) in recent …

Neural network ensemble is a learning paradigm where many neural networks are jointly
used to solve a problem. In this paper, the relationship between the ensemble and its …

Ensemble approaches to classification and regression have attracted a great deal of interest
in recent years. These methods can be shown both theoretically and empirically to …

This paper presents a learning approach, ie negative correlation learning, for neural network
ensembles. Unlike previous learning approaches for neural network ensembles, negative …

Rapid developments in the field of genetic algorithms along with the popularity of the first
edition precipitated this completely revised, thoroughly updated second edition of The …

This paper presents an evolutionary artificial neural network (EANN) approach based on the
pareto-differential evolution (PDE) algorithm augmented with local search for the prediction …

In this paper, an efficient method based on a new fuzzy neural network is proposed for short-
term price forecasting of electricity markets. This fuzzy neural network has inter-layer and …