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

This booklet constitutes the refereed lawsuits of the eleventh overseas convention on man made 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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Xn ) is an interval vector. We note m(X) = (m(X1 ), . . , m(Xn )) its midpoint. We note (X, Y ) the convex hull of two boxes X and Y , that is the smallest box that contains X and Y . In the following, capital letters represent interval quantities (interval X) and bold letters represent vectors (box X, vector x). Definition 2 (Interval extension; Natural interval extension). Let f : Rn → R be a real-valued function. F : IRn → IR is an interval extension of f if ∀X ∈ IRn , f (X) = {f (x) | x ∈ X} ⊂ F (X) ∀(X, Y) ∈ IRn , X ⊂ Y ⇒ F (X) ⊂ F (Y) The natural interval extension FN is obtained by replacing the variables with their domains and real elementary operations with interval arithmetic operations.

The performance of a heuristic search algorithm on this problem is also analyzed. In particular, we study the correlation between local optima network features and the performance of an iterated local search heuristic. Our analysis reveals that network features can explain and predict problem difficulty. The evidence confirms the superiority of the insertion operator for this problem.

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. This result is extended to an adaptive rule of resamplings (Sect. 3), in which the number of evaluations depend on the step size only; we also get rid of the scale invariant assumption.

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