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By Heike Trautmann, Günter Rudolph, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, Christian Grimme

This booklet constitutes the refereed complaints of the ninth overseas convention on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017.

The 33 revised complete papers offered including thirteen poster shows have been conscientiously reviewed and chosen from seventy two submissions. The EMO 2017 goals to debate all elements of EMO improvement and deployment, together with theoretical foundations; constraint dealing with innovations; choice dealing with ideas; dealing with of constant, combinatorial or mixed-integer difficulties; neighborhood seek thoughts; hybrid techniques; preventing standards; parallel EMO versions; functionality evaluate; try out capabilities and benchmark difficulties; set of rules choice methods; many-objective optimization; huge scale optimization; real-world functions; EMO set of rules implementations.

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Extra resources for Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings

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Metaheuristics using this interface can be empowered with different evaluator implementations, so that the current way of evaluating solutions is transparent to the algorithms. evaluate(population, problem); return population; } In this way, the actual evaluator is instantiated when configuring the settings of the metaheuristic, so no changes in the code are needed. jMetal 5 currently includes two implementations of SolutionListEvaluator: sequential and multithreaded. Our approach has been then to develop an evaluator based on Spark.

An algorithm scales linearly (ideal) when it reaches a speedup SN = N and hence, the parallel efficiency is EN = 100%. 2 Computational Effort As stated, to measure the parallel computing performance of our approach we have used the NSGA-II algorithm to solve a modified version of the ZDT1 problem. The running time of NSGA-II with those settings in a laptop equipped with an Intel i7 processor is less than a second, so we have artificially increased the computing time of the evaluation functions of ZDT1 (by adding an idle loop) to simulate a real scenario where the total computing time would be in the order of several hours.

Comparasion of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000) 14. : GDE3: the third evolution step of generalized differential evolution. In: IEEE Congress on Evolutionary Computation (CEC 2005), pp. 443–450 (2005) 15. : Google’s MapReduce programming model revisited. Sci. Comput. Program. 70(1), 1–30 (2008) 30 C. Barba-Gonzal´ez et al. 16. : Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

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