Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offsp...
In this paper we present a concurrent implementation of genetic algorithms designed for shared memory architectures intended to take advantage of multi-core processor platforms. O...
The paper presents an approach based on principles of immune systems to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by b...
Marek Ostaszewski, Franciszek Seredynski, Pascal B...
The aim of this work is to analyze the applicability of crowding differential evolution to unsupervised clustering. The basic idea of this approach, interpreting the clustering pr...
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we pre...