Download Artificial Evolution: 11th International Conference, by Pierrick Legrand, Marc-Michel Corsini, Jin-Kao Hao, Nicolas PDF

By Pierrick Legrand, Marc-Michel Corsini, Jin-Kao Hao, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer

This ebook constitutes the refereed complaints of the eleventh overseas convention on synthetic Evolution, EA 2013, held in Bordeaux, France, in October 2013. The 20 revised papers have been conscientiously reviewed and chosen from 39 submissions. The papers are concentrated to idea, ant colony optimization, purposes, combinatorial and discrete optimization, memetic algorithms, genetic programming, interactive evolution, parallel evolutionary algorithms, and swarm intelligence.

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Extra resources for Artificial Evolution: 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers

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Comput. 3(1), 37–51 (2004) 8. : On the approximability of trade-offs and optimal access of web sources. In: Symposium on Foundations of Computer Science (FOCS 2000), pp. 86–92. IEEE (2000) 9. : A two-phase heuristic for the biobjective 0/1 knapsack problem. Unpublished (2008) 10. : Clusters of non-dominated solutions in multiobjective combinatorial optimization: an experimental analysis. , et al. ) Multiobjective Programming and Goal Programming. Lecture Notes in Economics and Mathematical Systems, vol.

8768 (2013) 21. : COPSO: constrained optimization via PSO algorithm. Technical report, CIMAT (2007) 22. : The Baldwin effect as an optimization strategy. Technical report, CIMAT (2007) 23. : A brief comparison of some evolutionary optimization methods. In: Proceedings of the Conference on Applied Decision Technologies. Modern Heuristic Search Methods, Uxbridge, 1995, pp. 255–272. Wiley, Chichester (1996) 24. : Minimization of Keane‘s bump function by the repulsive particle swarm and the differential evolution methods.

Also evolution strategies, contrarily to algorithms with good nonuniform rates, have a nice empirical behavior from the point of view of uniform rates, as shown mathematically by [18]. Overview of the paper. In this paper we show mathematical proofs and experimental results on the convergence of the evolutionary algorithms that will be described in the following sections, which include some resampling rules aiming to cancel the effect of noise. The theoretical analysis presents an exponential number of resamplings together with an assumption of scale invariance.

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