Parallel Problem Solving from Nature, PPSN XI: 11th by Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter PDF

By Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter Rudolph

ISBN-10: 3642158706

ISBN-13: 9783642158704

This e-book constitutes the refereed lawsuits 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 a variety of issues, from evolutionary computation to swarm intelligence, from bio-inspired computing to genuine global functions. computing device studying and mathematical video games supported via evolutionary algorithms in addition to memetic, agent-oriented structures also are represented.

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Read Online or Download Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part II PDF

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Additional resources for Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part II

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In each island, a serial MOEA is executed for a predefined number of generations called epoch. At the end of an epoch, communication between neighboring islands is allowed. In this communication, individuals (or copies of them in the case of pollination) can migrate from its 1 Our discussion here is focused exclusively on MOEAs that use exact objective function values, since fitness approximation schemes and surrogate models can also be used to deal with expensive MOPs. pMODE-LD+SS: A Parallel Multi-Objective DE-Based Algorithm 23 actual island to a different one according to a predefined migration topology which determines the migration path along which individuals can move.

References 1. : Evolutionary Many-Objective Optimization: A Short Review. In: Proc. IEEE Congress on Evolutionary Computation, pp. 2424–2431. IEEE Service Center (2008) 2. : Indicator-based Selection in Multiobjective Search. -P. ) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004) 3. : An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. , Zitzler, E. ) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005) 4. : Substitute Distance Assignments in NSGA-II for Handling Many-Objective Optimization Problems.

Journal of Machine Learning Research 7, 1–30 (2006) 20. : The Measure of Pareto Optima Applications to Multi-objective Metaheuristics. , Thiele, L. ) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003) 21. : Handling multiple objectives with particle swarm optimization. IEEE Trans. jp Abstract. This work proposes a hybrid strategy in a two-stage search process for many-objective optimization. The first stage of the search is directed by a scalarization function and the second one by Pareto selection enhanced with Adaptive -Ranking.

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Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part II by Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter Rudolph


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