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This e-book constitutes the refereed complaints of the eleventh overseas convention on Parallel challenge fixing from Nature - PPSN XI, held in Kraków, Poland, in September 2010. The 131 revised complete papers have been rigorously reviewed and chosen from 232 submissions. The convention covers quite a lot of issues, from evolutionary computation to swarm intelligence, from bio-inspired computing to genuine global functions. desktop studying and mathematical video games supported by means of evolutionary algorithms in addition to memetic, agent-oriented structures also are represented.
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Additional resources for Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I
The log-linear convergence of the ES is preserved. Both methods lead to an improvement and in combination the (1,4)-ES becomes about 10% faster than the (1+1)-ES. Naively implemented into the CMA-ES with recombination, mirrored sampling leads to a bias on the step-size. However, the (1,4)-CMA-ES with mirrored sampling and sequential selection is unbiased and appears to be faster, more robust, and as local as the (1+1)-CMA-ES. 1 Introduction Evolution strategies (ESs) are robust stochastic search algorithms designed to minimize objective functions f that map a continuous search space Êd into Ê.
With a distribution of processing times inferred from the instances), tuning will typically be biased towards some speciﬁc algorithm B, with an ideal mean performance over this instance space. However, there is an arbitrarily large number of coherent subsets of this space (deﬁned by, for example, a given mean task length, or a given value for the range of processing times, and so forth) on which B’s performance may well by trumped by an alternative conﬁguration. The extent to which B may under-perform on new instances could be dramatic.
However one clear diﬀerence is that fewer distinct algorithms are represented as winners of the subspaces - the mean number of subspaces per winning algorithm was generally c. 3 in the SMT footprints, but c. 4 for the VRP. Also, the appearance of these footprints suggests regionalisation, but in fact the level of neighbour similarity is insigniﬁcant in both cases (again deterined by randomisation tests). e. there is no more regionalisation than would be expected for a random permutation of the same distribution of winning algorithms within the subspaces.