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 …

Fuzzy sets were first proposed by Lotfi Zadeh in his seminal paper [366] in 1965, and ever
since have been a center of many discussions, fervently admired and condemned. Both …

Many real-world problems involve the simultaneous optimization of several competing
objectives and constraints that are difficult, if not impossible, to solve without the aid of …

There are a number of problems associated with training neural networks with
backpropagation algorithm. The algorithm scales exponentially with increased complexity of …

Evolutionary algorithms are a class of stochastic search methods that attempts to emulate
the biological process of evolution, incorporating concepts of selection, reproduction, and …

Abstract In this paper, ARIMA (1, 1, 1) model and Artificial Neural Network (ANN) models like
Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and …

In the typical nonparametric approach to classification in instance-based learning and data
mining, random data (the training set of patterns) are collected and used to design a …

In pattern classification problems, many works have been carried out with the aim of
designing good classifiers from different perspectives. These works achieve very good …

The stability of underground entry-type excavations (UETEs) is of paramount importance for
ensuring the safety of mining operations. As more engineering cases are accumulated …

Stochastic gradient descent (SGD) is a premium optimization method for training neural
networks, especially for learning objectively defined labels such as image objects and …