Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks
and problem domains. However, the success of DNNs depends on the proper configuration …

A new class of evolutionary computation processes is presented, called Learnable Evolution
Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …

Because stochastic gradient descent (SGD) has shown promise optimizing neural networks
with millions of parameters and few if any alternatives are known to exist, it has moved to the …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …