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

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.)

This e-book constitutes the lawsuits of the ninth overseas convention on man made Evolution, held in Strasbourg, France, in October 2009.

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Read 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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We used a greedy search algorithm with a first improvement 1-swap neighborhood and a don’t look bit mechanism [2]. e. in the configurations with a smaller number of colonies a bigger number of solutions per colony were subjected to local search). In the migrate best solution step, for each colony we consider only the best solution found in the present iteration. We use these solutions to determine 30 L. Melo, F. Pereira, and E. Costa the best and worst colonies. Let hdbest and hdworst stand for the value of the solution found by the best and worst colonies respectively.

C Springer-Verlag Berlin Heidelberg 2010 26 L. Melo, F. Pereira, and E. Costa [16]. One advantage of this type of network is the ability to create a virtual topology different from the underlying physical topology. The assignment of the physical nodes to the virtual topology is a strong factor in the efficiency of the network. The results obtained are encouraging as they show the advantage provided by the existence of several colonies. Migration is able to enhance the algorithm performance without causing the convergence of the colonies to the same trail.

Ant System (AS) [6], [7] was the first ACO algorithm. Since then some some variants have been derived, being the MAX-MIN Ant System (MMAS) [19] and Ant Colony System (ACS) [5] some of the most successful and most studied of them [8]. A common characteristic of ACS and MMAS is that they focus their search in a specific region of the search space [8]. We thus hope that by using an island model approach a bigger portion of the landscape can be covered. Our method is inspired in the ACS, partly because is considered the most aggressive of the two [8] and is able to find better solutions in short computation times, although it converges sooner to a suboptimal solution.

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