title: A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.
abstract: 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.
authors.affiliation: Universidad Nacional de la Patagonia Austral, Ruta 3 Acceso Norte, s/n, Caleta Olivia, 9011 Santa Cruz, Argentina.
authors.forename: Andrea
authors.identifier: 0000-0003-1285-2959
authors.initials: A
authors.lastname: Villagra
authors.affiliation: Universidad de Málaga, Campus Teatinos, 29071 Málaga, Spain.
authors.forename: Enrique
authors.initials: E
authors.lastname: Alba
authors.affiliation: Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Leguizamón
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D006801:Humans; D008958:Models, Molecular; D057386:Search Engine / Q000379:methods*; D018709:Statistics, Nonparametric