KaKaRaKe - User-Friendly Visualization for Multiobjective Optimization with High-Dimensional Objective Vectors
Dächert, K., Klamroth, K., Miettinen, K., & Steuer, R. E. (2020). KaKaRaKe - User-Friendly Visualization for Multiobjective Optimization with High-Dimensional Objective Vectors. In C. M. Fonseca, K. Klamroth, G. Rudolph, & M. M. Wiecek (Eds.), Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031) (10, pp. 97-103). Dagstuhl Publishing. Dagstuhl Reports. https://drops.dagstuhl.de/opus/volltexte/2020/12401/pdf/dagrep_v010_i001_p052_20031.pdf
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Dagstuhl ReportsDate
2020Discipline
TietotekniikkaMultiobjective Optimization GroupLaskennallinen tiedePäätöksen teko monitavoitteisestiMathematical Information TechnologyMultiobjective Optimization GroupComputational ScienceDecision analytics utilizing causal models and multiobjective optimizationCopyright
© Kerstin Dächert, Kathrin Klamroth, Kaisa Miettinen, and Ralph E. Steuer
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Dagstuhl PublishingConference
Dagstuhl SeminarIs part of publication
Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)ISSN Search the Publication Forum
2192-5283
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https://drops.dagstuhl.de/opus/volltexte/2020/12401/pdf/dagrep_v010_i001_p052_20031.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/41576868
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Except where otherwise noted, this item's license is described as CC BY 3.0