Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …

Improving the performance of optimization algorithms is a trend with a continuous growth,
powerful and stable algorithms being always in demand, especially nowadays when in the …

Differential evolution (DE) has been extensively used in optimization studies since its
development in 1995 because of its reputation as an effective global optimizer. DE is a …

Motivated by the recent success of diverse approaches based on differential evolution (DE)
to solve constrained numerical optimization problems, in this paper, the performance of this …

A modified Artificial Bee Colony algorithm to solve constrained numerical optimization
problems is presented in this paper. Four modifications related with the selection …

Metaheuristic methods have been demonstrated to be efficient tools to solve hard
optimization problems. Most metaheuristics define a set of parameters that must be tuned. A …

In this paper we develop a Self-guided Genetic Algorithm (Self-guided GA), which belongs
to the category of Estimation of Distribution Algorithms (EDAs). Most EDAs explicitly use the …

Differential evolution (DE), belonging to the evolutionary algorithm class, is a simple and
powerful optimizer with great potential for solving different types of synthetic and real-life …

Software testing is a crucial part of software development. It enables quality assurance, such
as correctness, completeness and high reliability of the software systems. Current state-of …