Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

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 …

Recently, convolutional neural networks (CNNs) have achieved great success in the field of
artificial intelligence, including speech recognition, image recognition, and natural language …

With the development of deep learning, the design of an appropriate network structure
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …

This article provides an overview of the mainstream deep learning approaches and research
directions proposed over the past decade. It is important to emphasize that each approach …

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 …

Metalearning attracted considerable interest in the machine learning community in the last
years. Yet, some disagreement remains on what does or what does not constitute a …

A back-propagation (BP) neural network has good self-learning, self-adapting and
generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of …

This paper evaluates the four leading techniques proposed in the literature for construction
of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian …

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution,
artificial neural networks are trained through evolutionary algorithms, taking inspiration from …