New PDF release: Evolutionary Multi-Criterion Optimization: 9th International

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

ISBN-10: 3319541560

ISBN-13: 9783319541563

ISBN-10: 3319541579

ISBN-13: 9783319541570

This ebook constitutes the refereed court cases of the ninth foreign convention on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017.

The 33 revised complete papers provided including thirteen poster shows have been conscientiously reviewed and chosen from seventy two submissions. The EMO 2017 goals to debate all points of EMO improvement and deployment, together with theoretical foundations; constraint dealing with innovations; choice dealing with recommendations; dealing with of continuing, combinatorial or mixed-integer difficulties; neighborhood seek strategies; hybrid techniques; preventing standards; parallel EMO versions; functionality assessment; try out capabilities and benchmark difficulties; set of rules choice techniques; 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

Sample text

This behavior was somewhat expected as the parallel model is based on alternating parallel and sequential steps. Considering the results, it is worth mentioning that both problem configurations yield similar speedup and efficiency values, which indicates that the bottleneck is due to both the parallel model and the parallel infrastructure, so increasing the evaluation time does not compensate the synchronization and communication costs. 3 25 Data Block Size We now turn to a number of experiments to measure the influence of using different data block sizes (file sizes) on the evaluation process of NSGA-II.

The attractive point of the adopted approach is that a number of single and multi-objective metaheuristics can be executed in parallel without requiring any modification. If we look closely at steps 3 and 6 in Algorithm 1 we can see that the set S can be evaluated in parallel by the Spark-based evaluator, so the resulting parallel model is a heartbeat algorithm: a parallel step is alternated with a sequential one (for the rest of the phases in the main loop of the metaheuristic). 22 C. Barba-Gonzal´ez et al.

Since finding the Pareto front is often intractable, EMO algorithms attempt to find a high-quality approximation of it, namely, an approximation front composed of mutually nondominated objective vectors. Hence, the performance assessment of EMO algorithms requires the evaluation of the relative quality of approximation fronts. Although Pareto-optimality may sometimes be enough to conclude that one approximation front is better than another, the most common case is that fronts are mutually incomparable.

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Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings by Heike Trautmann, Günter Rudolph, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, Christian Grimme


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