A4 Article in conference proceedings
Data-Driven Interactive Multiobjective Optimization Using a Cluster-Based Surrogate in a Discrete Decision Space (2019)


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). Cham: Springer. doi:10.1007/978-3-030-13709-0_9


JYU authors or editors


Publication details

All authors or editors: Hakanen, Jussi; Malmberg, Jose; Ojalehto, Vesa; Eyvindson, Kyle

Parent publication: LOD 2018 : Machine Learning, Optimization, and Data Science. 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers

Parent publication editors: Nicosia, Giuseppe; Pardalos, Panos; Giuffrida, Giovanni; Umeton, Renato; Sciacca, Vincenzo

ISBN: 978-3-030-13708-3

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2019

Number in series: 11331

Pages range: 104-115

Number of pages in the book: 562

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: http://doi.org/10.1007/978-3-030-13709-0_9

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/66476

Additional information: Also part of the Information Systems and Applications, incl. Internet/Web, and HCI book sub series (LNISA, volume 11331).


Abstract

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.


Keywords: optimisation; clusters; decision making; silviculture; boreal zone

Free keywords: data-driven optimization; surrogates; clustering; preference information; decision maker; boreal forest management


Contributing organizations


Related projects

Decision Support for Computationally Demanding Optimization Problems
Miettinen, Kaisa
Academy of Finland
01/09/2015-31/08/2019


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2020-18-10 at 21:25