A New Paradigm in Interactive Evolutionary Multiobjective Optimization
Saini, B. S., Hakanen, J., & Miettinen, K. (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature (pp. 243-256). Springer. Lecture Notes in Computer Science, 12270. https://doi.org/10.1007/978-3-030-58115-2_17
Published inLecture Notes in Computer Science
© Springer Nature Switzerland AG 2020
Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, where preference information is directing the solution process. Advantages of optimizing in this new space are discussed and the idea is demonstrated with two interactive evolutionary algorithms: IOPIS/RVEA and IOPIS/NSGA-III. According to the experiments conducted, the new algorithms provide solutions that are better in quality as compared to those of state-of-the-art evolutionary algorithms and their variants where preference information is incorporated in the original objective space. Furthermore, the promising results require fewer function evaluations. ...
Parent publication ISBN978-3-030-58114-5
ConferenceInternational Conference on Parallel Problem Solving From Nature
Is part of publicationPPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature
Publication in research information system
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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 (grant numbers 322221 and 311877). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.
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