The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

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 …

Metaheuristic algorithms are extensively recognized as effective approaches for solving high-
dimensional optimization problems. These algorithms provide effective tools with important …

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

Evolutionary algorithms (EAs) have been applied with success to many numerical and
combinatorial optimization problems in recent years. However, they often lose their …

This paper deals with the comparison of models for predicting porosity and permeability of
oil reservoirs by coupling a machine learning concept and petrophysical logs. Different …

Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in
numerous numbers of oil production applications like those in remote or unmanned …

In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large
scale optimization problems. The motivation is to improve our previous work on grou** …

Cooperative coevolution decomposes a problem into subcomponents and employs
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …