The problem of selecting the patterns to be learned by any model is usually not considered
by the time of designing the concrete model but as a preprocessing step. Information theory …

Purpose–Popular regression methodologies are inapplicable to obtain accurate
metamodels for high dimensional practical problems since the computational time increases …

The design of radial basis function neural networks (RBFNNs) still remains as a difficult task
when they are applied to classification or to regression problems. The difficulty arises when …

This paper presents an evolutionary radial basis function neural network with genetic
algorithm and artificial immune system (GAAIS-RBFNN) for tracking control of autonomous …

Design optimization problems with black-box computation-intensive objective and
constraints are extremely challenging in engineering practices. To address this issue, an …

This paper presents a new evolutionary cooperative–competitive algorithm for the design of
Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO 2 …

Radial basis function neural networks are a widely used type of artificial neural network. The
number and centers of basis functions directly affect the accuracy and speed of radial basis …

The majority of clustering methods that have been proposed in the literature are focused on
classification problems, and more specifically on pattern recognition problems. In this paper …

Since 2007, providing government‐guided funds has gradually become an important policy
means to develop the sports industry. First, this study defined the concept of government …

In this paper, a diversity generating mechanism is proposed for an Evolutionary
Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron …