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 …

Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

Fuzzy rough theory can describe real-world situations in a mathematically effective and
interpretable way, while evolutionary neural networks can be utilized to solve complex …

Despite the impressive progress in neural network architecture design, improving the
performance of the existing state-of-the-art models has become increasingly challenging …

The scene classification approaches using deep learning have been the subject of much
attention for remote sensing imagery. However, most deep learning networks have been …

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 …

Our world is moving fast towards the era of the Internet of Things (IoT), which connects all
kinds of devices to digital services and brings significant convenience to our lives. With the …

Memetic computing is a subject in computer science which considers complex structures
such as the combination of simple agents and memes, whose evolutionary interactions lead …

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs
employ artificial recurrent neural networks in a way that has recently been proposed …