Using a Database to Support Interactive Multiobjective Optimization, Visualization, and Analysis
Saini, B. S., Lárraga, G., & Miettinen, K. (2023). Using a Database to Support Interactive Multiobjective Optimization, Visualization, and Analysis. In GECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp. 1703-1711). ACM. https://doi.org/10.1145/3583133.3596383
Date
2023Discipline
Multiobjective Optimization GroupLaskennallinen tiedeHyvinvoinnin tutkimuksen yhteisöPäätöksen teko monitavoitteisestiMultiobjective Optimization GroupComputational ScienceSchool of WellbeingDecision analytics utilizing causal models and multiobjective optimizationCopyright
© 2023 Copyright held by the owner/author(s).
Many libraries of open-source implementations of multiobjective optimization problems (MOPs) and evolutionary algorithms (MOEAs) have been developed in recent years. These libraries enable researchers to solve their MOPs using diverse MOEAs. Some libraries also implement interactive MOEAs, which enable decision-makers (experts in the domain of the MOP) to provide their preferences and guide the optimization process toward their region of interest. These libraries also provide access to visualization methods and benchmarking tools. However, they do not currently implement a database to store and utilize the data generated while running MOEAs.
We propose the creation of SIVA DB, a database designed to be easily incorporated into existing libraries as a modular addition. SIVA DB provides a standard way to archive an MOEA's population and the metadata associated with each population member. Such metadata can include, e.g., the parameters and state of the MOEA and the preferences the decision-maker gives (in the case of interactive MOEAs). The database can store data from multiple runs of any number of MOEAs, and even data from different MOPs. SIVA DB provides easy access to the contained data to analyze the optimization process or create efficient MOEAs. We demonstrate the latter in this paper with experiments.
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ACMParent publication ISBN
979-8-4007-0120-7Conference
Genetic and Evolutionary Computation ConferenceIs part of publication
GECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary ComputationKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/184213740
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Research Council of FinlandFunding program(s)
Academy Project, AoFAdditional information about funding
This research was partly funded by the Academy of Finland (grant 322221). The research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO), jyu.fi/demo, at the University of Jyvaskyla.License
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