From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

An artificial neural network (ANN) that mimics the information processing mechanisms and
procedures of neurons in human brains has achieved a great success in many fields, eg …

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 learning process of artificial neural networks is considered as one of the most difficult
challenges in machine learning and has attracted many researchers recently. The main …

Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired
by the collective behavior of social animals. In particle swarm optimization (PSO) the set of …

Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

We introduce the hyperparameter search problem in the field of machine learning and
discuss its main challenges from an optimization perspective. Machine learning methods …

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle
Swarm Optimization) to predict the energetically stable/metastable crystal structures of …

In this paper we present a conceptual framework for parameter tuning, provide a survey of
tuning methods, and discuss related methodological issues. The framework is based on a …