In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

This study proposes a technique aimed at the automatic search for parameter adaptation
strategies in a differential evolution algorithm with genetic programming symbolic …

Hyper-heuristic frameworks have emerged out of the shadows of meta-heuristic techniques.
In this very active field, new frameworks are developed all the time. Shared common …

Abstract This paper proposes Drone Squadron Optimization (DSO), a new self-adaptive
metaheuristic for global numerical optimization which is updated online by a hyper-heuristic …

Evolutionary programming can solve black-box function optimisation problems by evolving a
population of numerical vectors. The variation component in the evolutionary process is …

Parameter adaptation is one of the key research fields in the area of evolutionary
computation. In this study, the application of neuroevolution of augmented topologies to …

Software engineers are frequently faced with tasks that can be expressed as optimization
problems. To support them with automation, search-based model-driven engineering …

Restricting the class of problems we want to perform well on allows Black Box Search
Algorithms (BBSAs) specifically tailored to that class to significantly outperform more general …

For the last decades, metaheuristics have become ever more popular as a tool to solve a
large class of difficult optimization problems. However, determining the best configuration of …

Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively
young fields of research that have been receiving more attention in recent years. Both …