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Download PDF by I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, E.: Artifical Evolution: 9th International Conference, Evolution

By I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, E. Tarantino (auth.), Pierre Collet, Nicolas Monmarché, Pierrick Legrand, Marc Schoenauer, Evelyne Lutton (eds.)

ISBN-10: 3642141552

ISBN-13: 9783642141553

ISBN-10: 3642141560

ISBN-13: 9783642141560

This ebook constitutes the complaints of the ninth overseas convention on man made Evolution, held in Strasbourg, France, in October 2009.

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Read Online or Download Artifical Evolution: 9th International Conference, Evolution Artificielle, EA, 2009, Strasbourg, France, October 26-28, 2009. Revised Selected Papers PDF

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Extra resources for Artifical Evolution: 9th International Conference, Evolution Artificielle, EA, 2009, Strasbourg, France, October 26-28, 2009. Revised Selected Papers

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Melo, F. Pereira, and E. Costa 5. : Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997) 6. : Positive feedback as a search strategy. Tech. , Politecnico di Milano, Italy (1991) 7. : Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics 26(1), 29–41 (1996) 8. : Ant Colony Optimization. A Bradford Book. MIT Press, Cambridge (2004) 9. : Exchange strategies for multiple ant colony system.

S ¸ . Uyar, and J. Branke 5. : A two-phase genetic algorithm for large-scale bidline-generation problems at delta air lines. Interfaces 29(5), 51–65 (1999) 6. : An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: 2002 Congress on Evolutionary Computation, vol. 2, pp. 1185–1190. IEEE, Los Alamitos (2002) 7. : Efficiently solving: A large-scale integer linear program using a customized genetic algorithm. , et al. ) GECCO 2004. LNCS, vol. 3102, pp. 1054–1065.

5 we present two examples of trail differences (averaged over 30 runs each): in panel a) we display results for the p09 instance from the n = 16 × 16 data set; in panel b) we can see the results for the p08 instance from the n = 8 × 8 data set. The same trend is visible for the other instances. Initially all colonies have the same trail and, as expected, in the early stages of the optimization they become distinct. Results form the chart depicted in fig. 5 reveal that the trails are able to remain separated until the end of the execution.

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Artifical Evolution: 9th International Conference, Evolution Artificielle, EA, 2009, Strasbourg, France, October 26-28, 2009. Revised Selected Papers by I. De Falco, A. Della Cioppa, D. Maisto, U. Scafuri, E. Tarantino (auth.), Pierre Collet, Nicolas Monmarché, Pierrick Legrand, Marc Schoenauer, Evelyne Lutton (eds.)


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