During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process computationally intensive and requires an extensive search infer best hyperparameter set for given problem. The challenge exacerbated by fact that most ML models are complex internally, involves trial-and-error processes could remarkably affect predictive result....

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

Evolutionary computation is an area within the eld of arti cial intelligence that is founded upon the principles of biological evolution. Evolution can be de ned as the process of gradual development. Evolutionary algorithms are typically applied as a generic problem solving method, searching a problem space in order to locate good solutions. These solutions are found through an iterative evolu...

Memetic algorithms are popular approaches to improve pure evolutionary methods. But were and when in the system the local search should be applied and does it really speed up evolutionary search is a still an open question. In this paper we investigate the influence of the memetic extensions on globally induced regression and model trees. These evolutionary induced trees in contrast to the typi...

Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering val...

The hybridization of both, genetic algorithms and local search methods, has been shown to be an eeective route to follow for nding high quality solutions for hard optimization problems. Previously proposed genetic local search approaches for the traveling salesman problem focused on the importance of using a high quality local search method in order to yield high quality solutions. This paper p...