Download e-book for iPad: Artificial Evolution: 10th International Conference, by Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao

By Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao Hao, Pierrick Legrand, Pierre Collet, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer (eds.)

ISBN-10: 3642355323

ISBN-13: 9783642355325

ISBN-10: 3642355331

ISBN-13: 9783642355332

This publication constitutes chosen top papers from the tenth overseas convention on man made Evolution, EA 2011, held in Angers, France, in October 2011. before everything, 33 complete papers and 10 submit papers have been conscientiously reviewed and chosen from sixty four submissions. This ebook provides the nineteen top papers chosen from those contributions. The papers are prepared in topical sections on ant colony optimization; multi-objective optimization; research; implementation and robotics; combinatorial optimization; studying and parameter tuning; new nature encouraged versions; probabilistic algorithms; thought and evolutionary seek; and applications.

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Read or Download Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers PDF

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Extra info for Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers

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However, to the authors’ knowledge, third- and higher-order attainment functions have not yet been considered in practice, due to computational difficulties [5,6]. The quality indicator approach addresses the complexity of the outcome-set distribution in a different way, through the definition of so-called (unary) quality indicators which somehow transform each realized outcome set into a scalar. The corresponding, much simpler, univariate distributions of indicator values are usually studied via an estimate of their expected value (average indicator value), although other statistics could be considered as well.

M. Fonseca k 2. For a discrete reference set Zref = {z1 , . . , zk } of k not necessarily nond dominated points in R , the definition simplifies to k k )= ICF (X, Zref 1 · I{X k j=1 zj } . (8) In any case, the covered fraction indicator of X takes realizations in [0, 1], where a larger observed indicator value is considered to represent a “better” optimization result (in the particular sense of the indicator). 2 Expected Value Due to the “linearity property of expectation”, and since the binary random variable I{X z} follows a Bernoulli distribution with expected value P (X z) = αX(z), for all z ∈ Rd , it can be seen that the expected value of the covered fraction indicator distribution is related to the first-order attainment function.

355]. Note that a countable union of Hausdorff j-dimensional sets preserves the Hausdorff dimension [4, p. e. any such union of smooth sets is also of integer Hausdorff dimension. A nonmathematical discussion of the concept of “smoothness” can be found in [3, p. 335]. The Hausdorff dimension of any realization of the outcome set X is equal to zero, and the Hausdorff dimension of a non-empty attained set realization is equal to d. Non-smooth reference sets, such as fractal sets, may have a non-integer Hausdorff dimension.

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Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers by Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao Hao, Pierrick Legrand, Pierre Collet, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer (eds.)


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