By Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao Hao, Pierrick Legrand, Pierre Collet, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer (eds.)

This booklet constitutes chosen most sensible papers from the tenth overseas convention on man made Evolution, EA 2011, held in Angers, France, in October 2011. in the beginning, 33 complete papers and 10 put up papers have been conscientiously reviewed and chosen from sixty four submissions. This booklet provides the nineteen most sensible papers chosen from those contributions. The papers are equipped in topical sections on ant colony optimization; multi-objective optimization; research; implementation and robotics; combinatorial optimization; studying and parameter tuning; new nature encouraged versions; probabilistic algorithms; thought and evolutionary seek; and applications.

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Extra resources for Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers

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LNCS, vol. 1993, pp. 213–225. Springer, Heidelberg (2001) 9. Ito, K. ): Encyclopedic Dictionary of Mathematics 2. The Mathematical Society of Japan. The MIT Press (1987) 10. : Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Kluwer Academic Publishers, Dordrecht (2004) 11. : Theory of Random Sets. Springer, London (2005) 12. : Chaos in Dynamical Systems. Cambridge University Press, Cambridge (2002) 13. : MOSA method: A tool for solving multiobjective combinatorial optimization problems.

Xn , . . randomly independently uniformly distributed in [0, 1]D and > 0. Then E[inf {n ∈ N; Disp(x, n) < ] = θ 1/ D log(1/ ) . From Proposition 1, and the complexity of dispersions above, it comes Corollary 1 (Complexity in terms of number of restarts). Let (F (k) )k∈N be a family of sets of fitness functions defined on [0, 1]D for some D. Suppose (k) that for each k ∈ N, F (k) has strong local property (Eq. 3) with values (k) , σ0 . Then, for some C > 0, a quasi-random restart with Halton sequence4 and σi < σ0 ensures that (∀k ∈ N), (∀f ∈ F (k) ), (X ∗ (f ) ⊂ {ES(xi , σi , f ); i ∈ [[1, C/ This is not true for random restart, and C/ 3 4 D D ]]}).

Scaling, Self-similarity, and Intermediate Asymptotics. Cambridge University Press, Cambridge (1996) 4. : Real Analysis. Birkh¨ auser, Boston (2002) 5. : Exploring the Performance of Stochastic Multiobjective Optimisers with the Second-Order Attainment Function. , Zitzler, E. ) EMO 2005. LNCS, vol. 3410, pp. 250–264. Springer, Heidelberg (2005) 6. : On the Computation of the Empirical Attainment Function. , Greco, S. ) EMO 2011. LNCS, vol. 6576, pp. 106–120. Springer, Heidelberg (2011) 7. : The attainment-function approach to stochastic multiobjective optimizer assessment and comparison.

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