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 …

The main focus of this paper is on the family of evolutionary algorithms and their real-life
applications. We present the following algorithms: genetic algorithms, genetic programming …

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 …

Differentiable neural architecture search (DARTS), as a gradient-guided search method,
greatly reduces the cost of computation and speeds up the search. In DARTS, the …

One of the major difficulties facing researchers using neural networks is the selection of the
proper size and topology of the networks. The problem is even more complex because often …

In this study, a probabilistic neural network (PNN) is used to estimate the state of health
(SOH) of Li-ion batteries. The accurate prediction of SOH can help avoid inconveniences or …

This paper deals with a distributed blocking flowshop scheduling problem, which tries to
solve the blocking flowshop scheduling in distributed manufacturing environment. The …

Many real-world optimization problems are difficult to solve as they do not possess the nice
mathematical properties required by the exact algorithms. Evolutionary algorithms are …

Multi-layer neural networks (MLNNs) are extensively used in many industrial applications.
Training is the crucial task for MLNNs. While gradient descent-based approaches are most …

This paper proposes a novel control system combining adaptively feed-forward neural
controller and PID controller to control the joint-angle position of the SCARA parallel robot …