A memetic algorithm with self-adaptive local search

Título:

A memetic algorithm with self-adaptive local search : TSP as a case study

Autor:

Krasnogor, Natalio

Colaboradores:

; Smith, Jim

Temas:

ALGORITMOS

En:

International Genetic and Evolutionary Computation Conference (2000 jul., 8-12 : Las Vegas), pp. 897-994.

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.

URL/DOI:

http://goo.gl/HYAhO8

Medio:

Soporte electrónico

Tipo de documento:

Artículo

Descripción física:

1 archivo (268,7 kB)

Idioma:

Inglés

Publicación:

, 2000

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