Evolution has provided a source of inspiration for algorithm designers since the birth of
computers. The resulting field, evolutionary computation, has been successful in solving …

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 …

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of
optimization problems. This flexibility makes them attractive for many optimization problems …

Hyperparameters of deep neural networks are often optimized by grid search, random
search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix …

This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for
constrained expensive black-box optimization that can be used for high-dimensional …

Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or
meta-models to approximate the fitness function in evolutionary algorithms (EAs), are …

Design of contemporary antenna structures heavily relies on electromagnetic (EM)
simulations. Accurate reflection and radiation responses of many antenna geometries can …

Automated design of dispatching rules for production systems has been an interesting
research topic over the last several years. Machine learning, especially genetic …

Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …