Global random search algorithms are characterized by using probability distributions to
optimize problems. Among them, generative methods iteratively update the distributions by …

Metaheuristics have gained great success in academia and practice because their search
logic can be applied to any problem with available solution representation, solution quality …

Differential evolution (DE) is a widely recognized method to solve complex optimization
problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from …

Problems with solutions represented by permutations are very prominent in combinatorial
optimization. Thus, in recent decades, a number of evolutionary algorithms have been …

Stochastic optimization algorithms have been successfully applied in several domains to
find optimal solutions. Because of the ever-growing complexity of the integrated systems …

Benchmarking provides an essential ground base for adequately assessing and comparing
evolutionary computation methods and other optimization algorithms. It allows us to gain …

Meta-heuristic stochastic optimization algorithms are predominantly used to solve complex
real-world problems. Numerous new nature-inspired meta-heuristics are being proposed to …

Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely
used in various optimization problems, known for their favorable results. Each generation of …

Practitioners frequently encounter the challenge of selecting the best optimization algorithm
from a pool of options. However, why not, rather than selecting a single algorithm, let …

Probability-based algorithms have proven to be a solid alternative for approaching
optimization problems. Nevertheless, in many cases, using probabilistic models that …