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
Published inLecture Notes in Computer Science
DisciplineEkologia ja evoluutiobiologiaTietotekniikkaEcology and Evolutionary BiologyMathematical Information Technology
© 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.
Parent publication ISBN978-3-030-13708-3
ConferenceInternational Conference on Machine Learning, Optimization, and Data Science
Is part of publicationLOD 2018 : Machine Learning, Optimization, and Data Science. 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Research profiles, AoF
Additional information about fundingThis 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.
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