Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods
Pour, P. A., Bandaru, S., Afsar, B., & Miettinen, K. (2022). Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods. In J. E. Fieldsend (Ed.), GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1803-1811). ACM. https://doi.org/10.1145/3520304.3533955
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Interactive methods support decision makers in finding the most preferred solution in multiobjective optimization problems. They iteratively incorporate the decision maker's preference information to find the best balance among conflicting objectives. Several interactive methods have been developed in the literature. However, choosing the most suitable interactive method for a given problem can prove challenging and appropriate indicators are needed to compare interactive methods. Some indicators exist for a priori methods, where preferences are provided at the beginning of the solution process. We present some numerical experiments that illustrate why these indicators are not suitable for interactive methods. As the main contribution of this paper, we propose a set of desirable properties of indicators for assessing interactive methods as the first step of filling a gap in the literature. We discuss each property in detail and provide simple examples to illustrate their behavior.
Parent publication ISBN978-1-4503-9268-6
ConferenceGenetic and Evolutionary Computation Conference
Is part of publicationGECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion
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 partly supported by the Academy of Finland (grant no 311877 and 322221) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multi-objective Optimization.
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