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

ISBN-10: 3319116827

ISBN-13: 9783319116822

ISBN-10: 3319116835

ISBN-13: 9783319116839

This ebook constitutes the refereed lawsuits of the eleventh foreign 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 centred 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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The global minima may be analytically computed for some separable or trivial functions, but for others (Rana and Egg Holder functions) no result concerning deterministic methods exists in the literature. Charibde has achieved new optimality results for three functions (Rana, Egg Holder and Michalewicz) and has proven the optimality of the known minima of the other functions. Table 1. 6344486869 Note that the constraints of Keane’s function do not contain variables with multiple occurrences, and are therefore not subject to dependency.

Example 2. Let us detail the first step of the IB&B on the problem min f (x) = x∈X x4 − 4x2 over the interval X = [−1, 4]. The natural interval extension of f is FN (X) = X 4 − 4X 2 and FN ([−1, 4]) = [−64, 256] ⊃ [−4, 192] = f ([−1, 4]). The floating-point evaluation f (1) = −3 yields an upper bound f˜ of f ∗ . Evaluating FN on the subinterval [3, 4] reduces the overestimation induced by the dependency effect: FN ([3, 4]) = [17, 220] ⊃ [45, 192] = f ([3, 4]). Since this enclosure is rigorous, ∀x ∈ [3, 4], f (x) ≥ 17 > f˜ = −3 ≥ f ∗ .

Algorithmica 59(3), 425–460 (2010) 28 S. Astete-Morales et al. 5. : A general noise model and its effects on evolution strategy performance. IEEE Trans. Evol. Comput. 10, 380–391 (2006) 6. : Noisy optimization: a theoretical strategy comparison of ES, EGS, SPSA & IF on the noisy sphere. In: GECCO, pp. 813–820 (2011) 7. : CLOP: confident local optimization for noisy Black-Box parameter tuning. , Plaat, A. ) ACG 2011. LNCS, vol. 7168, pp. 146–157. Springer, Heidelberg (2012) 8. : Handling expensive optimization with large noise.

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Artificial Evolution: 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers by Pierrick Legrand, Marc-Michel Corsini, Jin-Kao Hao, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer


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