Abstract
The interactive decision making (IDM) methods exploit the preference information from the decision maker during the optimization task to guide the search towards favourite solutions. This work measures the impact of inaccurate and contradictory preference information on the quality of the solutions generated by the IDM methods. The investigation is done in the context of the BC-EMO algorithm, a recently proposed multi-objective genetic algorithm.
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References
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Campigotto, P., Passerini, A. (2010). Adapting to a Realistic Decision Maker: Experiments towards a Reactive Multi-objective Optimizer. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_35
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DOI: https://doi.org/10.1007/978-3-642-13800-3_35
Publisher Name: Springer, Berlin, Heidelberg
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