In this article, we encompass an analysis of the recent advances in parallel genetic
algorithms (PGAs). We have selected these algorithms because of the deep interest in many …

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

Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …

Genetic algorithms are different from most other metaheuristics because they exploit three
key ideas:(1) the use of a population of solutions to guide search,(2) the use of crossover …

In gray-box optimization, the search algorithms have access to the variable interaction graph
(VIG) of the optimization problem. For Mk Landscapes (and NK Landscapes) we can use the …

The NK hybrid genetic algorithm (GA) for clustering is proposed in this paper. In order to
evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 …

Gray-box optimization requires user-supported information about inter-variable
dependencies to propose more effective optimizers for hard combinatorial problems. In …

AI methods are finding an increasing number of applications, but their often black-box nature
has raised concerns about accountability and trust. The field of explainable artificial …

In pseudo-Boolean optimization, a variable interaction graph represents variables as
vertices, and interactions between pairs of variables as edges. In black-box optimization, the …

We design, develop and analyze parallel variants of a state-of-the-art graybox optimization
algorithm, namely Drils (Deterministic Recombination and Iterated Local Search), for …