Download e-book for kindle: Evolutionary Multi-Criterion Optimization: 8th International by António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos

By António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello

ISBN-10: 3319158910

ISBN-13: 9783319158914

ISBN-10: 3319158929

ISBN-13: 9783319158921

This e-book constitutes the refereed court cases of the eighth foreign convention on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The sixty eight revised complete papers provided including four plenary talks have been rigorously reviewed and chosen from ninety submissions. The EMO 2015 goals to proceed those kind of advancements, being the papers offered centred in: theoretical features, algorithms improvement, many-objectives optimization, robustness and optimization less than uncertainty, functionality symptoms, a number of standards determination making and real-world applications.

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Read Online or Download Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II PDF

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Extra info for Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II

Example text

The hyper-plane is placed in a manner so that it intersects each objective axis at one. Das and reference points on the Dennis’s technique [5] is used to place H = M +p−1 p hyper-plane having (p + 1) points along each boundary. The population size N is chosen to be the smallest multiple of four greater than H, with the idea that for every reference point, one population member is expected to be found. At a generation t, the following operations are performed. First, the whole population Pt is classified into different non-domination levels, as it is done in NSGA-II as well, following the principle of non-dominated sorting.

Faster hypervolume-based search using Monte Carlo sampling. In: Proceedings of Multiple Criteria Decision Making (MCDM 2008). LNEMS, vol. 634, pp. 313–326. Springer, Heidelberg (2010) 3. : Principles of Optimization Theory. Narosa, New Delhi (2005) 4. : Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001) 5. : Scope of stationary multi-objective evolutionary optimization: A case study on a hydro-thermal power dispatch problem. Journal of Global Optimization 41(4), 479–515 (2008) 6.

Simulation results on a variety of mono, multi- and many-objective test problems are presented using U-NSGA-III and compared with a real-parameter genetic algorithm, NSGA-II and NSGA-III in Section 4. Finally, conclusions are drawn in Section 5. 2 A Brief Introduction to NSGA-III The proposed U-NSGA-III algorithm is based on the structure of NSGA-III, hence we first give a description of NSGA-III here. NSGA-III starts with a random population of size N and a set of widely-distributed pre-specified reference points H on a unit hyper-plane having a normal vector of ones.

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Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II by António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello


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