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

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 …

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 …

The parameter adaptation is one of the main problems in many evolutionary algorithms,
including differential evolution. Instead of manual development of new methods, a hyper …

hybrid model for evolving support vector machine (SVM) kernel functions is developed in
this paper. The kernel expression is considered as a parameter of the SVM algorithm and …

Crossover is a core genetic operator in many evolutionary algorithms (EAs). The
performance of such EAs on a given problem is dependent on properly configuring …

In many situations, an expert must visually analyze an image arranged in grey levels.
However, the human eye has strong difficulty in detecting details in this type of image …

Several applications result in a gray level image partitioned into different regions of interest.
However, the human brain has difficulty in recognizing many levels of gray. In some cases …