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

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

This booklet constitutes chosen top papers from the tenth foreign convention on man made Evolution, EA 2011, held in Angers, France, in October 2011. at the beginning, 33 complete papers and 10 publish 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 equipped 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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Additional info for Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers

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E[ICO (X, V, Zref )] = (20) Zref k 2. For a discrete reference set Zref = {z1 , . . , zk } of Hausdorff dimension zero, k αX(zj ) · P (V = zj ) . k E[ICO (X, V, Zref )] = (21) j=1 5 Hypervolume Indicator The hypervolume indicator (also known as dominated space), introduced in [16], considers the size of the portion of a deterministic, non-empty, closed reference set in objective space Rd that is attained by the outcome set X. Later, this idea was refined by including a weight function that assigns varying levels of importance to different regions of the reference set [14].

EMO 2001. LNCS, vol. 1993, pp. 213–225. Springer, Heidelberg (2001) 9. Ito, K. ): Encyclopedic Dictionary of Mathematics 2. The Mathematical Society of Japan. The MIT Press (1987) 10. : Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Kluwer Academic Publishers, Dordrecht (2004) 11. : Theory of Random Sets. Springer, London (2005) 12. : Chaos in Dynamical Systems. Cambridge University Press, Cambridge (2002) 13. : MOSA method: A tool for solving multiobjective combinatorial optimization problems.

As pointed out above, SAO had troubles to escape from local optima in multifrontal problems (ZDT4). While NSGA-II+SIVO clearly shows how the algorithm achieves a significant improvement with respect to the increase of the number of evaluations. Moreover, comparing both, the original NSGA-II and the NSGA-II+SIVO, it is clear that the SIVO operator helped to improve the convergence rate of NSGA-II+SIVO. The IGD results are shown in the Figure 4, in most of the problems the SAO algorithm obtained good results.

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