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Additional resources for Artificial Evolution: 11th International Conference, Evolution Artificielle, EA 2013, Bordeaux, France, October 21-23, 2013. Revised Selected Papers
The following corollary is here for showing that our result leads to the log-log convergence. 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.
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.
Seemingly results for ζ large are farther from the asymptotic regime. We conjecture that the asymptotic regime is −1/(2p) but that it is reached later when ζ is large. R-EDA  reaches −1/(2p); we seemingly get slightly better but this might be due to a non-asymptotic eﬀect. Figure 1 provides results with high numbers of evaluations. 4 −η Experiments with Adaptivity: Y σn Revaluations We here show experiments with Algorithm 2. The algorithm should converge linearly in log-log scale as shown by Corollary 3, at least for large enough values 26 S.