Get Artificial Evolution: 11th International Conference, PDF

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 overseas convention on synthetic Evolution, EA 2013, held in Bordeaux, France, in October 2013. The 20 revised papers have been rigorously reviewed and chosen from 39 submissions. The papers are concentrated to conception, ant colony optimization, purposes, combinatorial and discrete optimization, memetic algorithms, genetic programming, interactive evolution, parallel evolutionary algorithms, and swarm intelligence.

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

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Corollary 3: log-log convergence for adaptive resampling. With en η the number of evaluations at the end of iteration n, we have en = Y V1 exp(ρη) exp(ρηn)−1 exp(ρη)−1 . We then get, from Eq. 1, log(||xn ||)/ log(en ) → − α ρη (15) with probability at least 1 − δ. Equation 15 is the convergence in log/log scale. 3 Polynomial Number of Resamplings: Experiments We here consider a polynomial number of resamplings, as in Algorithm 3. Algorithm 3. An evolution strategy, with polynomial number of resamplings.

08 of Y and η. Notice that we consider values of μ, λ for which log-linear convergence is proved in the√noise-free setting (see Sect. In all this section, μ = min(d, λ/4 ), λ = d d . Slopes as estimated on the case η = 2 (usually the most favorable, and an important case naturally arising in sufficiently differentiable problems) are given in Table 1 (left) for dimension d = 100. In this case we are far from the asymptotic regime. g. [7]. In this case of high dimension we are far from the slope 1/(−2p), which might be the case for the asymptotic results.

T. gi (x) ≤ 0, i ∈ {1, . . , m} The current work extends the core method described in [6], in which we combined a GA and an IB&B that ran independently, and cooperated by exchanging information through shared memory in order to accelerate the convergence. In this framework, the GA quickly finds satisfactory solutions that improve the upper bound f˜ of the global minimum, and allows the IB&B to prune parts of the search-space more efficiently. The interval-based algorithm embedded in Charibde follows a Branch & Contract (IB&C) scheme (described in Algorithm 1), namely an IB&B algorithm that integrates a contraction step based on HC4Revise.

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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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