Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space
Hakanen, J., Malmberg, J., Ojalehto, V., & Eyvindson, K. (2019). Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space. In G. Nicosia, P. Pardalos, G. Giuffrida, R. Umeton, & V. Sciacca (Eds.), LOD 2018 : Machine Learning, Optimization, and Data Science. 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers (pp. 104-115). Springer. Lecture Notes in Computer Science, 11331. https://doi.org/10.1007/978-3-030-13709-0_9
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Lecture Notes in Computer ScienceDate
2019Discipline
Ekologia ja evoluutiobiologiaTietotekniikkaEcology and Evolutionary BiologyMathematical Information TechnologyCopyright
© Springer Nature Switzerland AG 2019
In this paper, a clustering based surrogate is proposed to be used in offline data-driven multiobjective optimization to reduce the size of the optimization problem in the decision space. The surrogate is combined with an interactive multiobjective optimization approach and it is applied to forest management planning with promising results.
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SpringerParent publication ISBN
978-3-030-13708-3Conference
International Conference on Machine Learning, Optimization, and Data ScienceIs part of publication
LOD 2018 : Machine Learning, Optimization, and Data Science. 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected PapersISSN Search the Publication Forum
0302-9743Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/28967579
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Academy of FinlandFunding program(s)
Academy Project, AoF; Research profiles, AoF
Additional information about funding
This research was supported by the Academy of Finland (projects no. 311877 and 287496) and is related to the thematic research area DEMO of the University of Jyväskylä.License
Except where otherwise noted, this item's license is described as In Copyright