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Computational Intelligence and Neuroscience
Volume 2016, Article ID 8289237, 11 pages
Research Article

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

1Universidad Nacional de la Patagonia Austral, Ruta 3 Acceso Norte, s/n, Caleta Olivia, 9011 Santa Cruz, Argentina
2Universidad de Málaga, Campus Teatinos, 29071 Málaga, Spain
3Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina

Received 29 January 2016; Accepted 15 May 2016

Academic Editor: Marc Van Hulle

Copyright © 2016 Andrea Villagra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.