A memetic algorithm with self-adaptive local search : TSP as a case study
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| Formato: | Capítulo de libro |
| Lengua: | inglés |
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| Acceso en línea: | http://goo.gl/HYAhO8 Consultar en el Cátalogo |
| Resumen: | In this paper we introduce a promising hybridization scheme for a Memetic Algorithm (MA). Our MA is composed of two optimization processes, a Genetic Algorithm and a Monte Carlo method (MC). In contrast with other GA-Monte Carlo hybridized memetic algorithms, in our work the MC stage serves two purposes: -- when the population is diverse it acts like a local search procedure and -- when the population converges its goal is to diversify the search. To achieve this, the MC is self-adaptive based on observations from the underlying GA behavior; the GA controls the long-term optimization process. We present preliminary, yet statistically significant, results on the application of this approach to the TSP problem.We also comment it successful application to a molecular conformational problem: Protein Folding. |
| Notas: | Formato de archivo: PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca) |
| Descripción Física: | 1 archivo (268,7 kB) |