The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …

The quantum-inspired evolutionary algorithm (QEA) applies several quantum computing
principles to solve optimization problems. In QEA, a population of probabilistic models of …

Probabilistic model-building algorithms (PMBA), a subset of evolutionary algorithms, have
been successful in solving complex problems, in addition providing analytical information …

Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization
that represent a more recent alternative to classical approaches such as evolutionary …

Estimation of Distribution Algorithms (EDAs) work by iteratively updating a distribution over
the search space with the help of samples from each iteration. Up to now, theoretical …

In our previous researches, we proposed the artificial chromosomes with genetic algorithm
(ACGA) which combines the concept of the Estimation of Distribution Algorithms (EDAs) with …

In this paper we propose a new probability update rule and sampling procedure for
population-based incremental learning. These proposed methods are based on the concept …

With apparently all research on estimation-of-distribution algorithms (EDAs) concentrated on
pseudo-Boolean optimization and permutation problems, we undertake the first steps …

Calibrating an evolutionary algorithm (EA) means finding the right values of algorithm
parameters for a given problem. This issue is highly relevant, because it has a high impact …

Estimation of Distribution Algorithms (EDAs) have recently been recognized as a prominent
alternative to traditional evolutionary algorithms due to their increasing popularity. The core …