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 …

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

In numerous industrial applications where safety, efficiency, and reliability are among
primary concerns, condition-based maintenance (CBM) is often the most effective and …

Deep convolutional networks (ConvNets) have achieved unprecedented performances on
many computer vision tasks. However, their adaptations to crowd counting on single images …

Soft sensors for regression applications (SSR) are inferential models that use online
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been
widely used to solve various scientific and engineering problems because of to their …

The question of how many hidden layers and how many hidden nodes should there be
always comes up in any classification task of remotely sensed data using neural networks …

Using surrogate models in evolutionary search provides an efficient means of handling
today's complex applications plagued with increasing high-computational needs. Recent …