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- W3192376399 abstract "Obtaining the optimal Pareto front in multiobjective portfolio problems can be impractical when considering real- world constraints, such as portfolio cardinality. Thus, it is interesting to consider multiobjective evolutionary algorithms to solve this type of problem in a reasonable time, but without quality guarantees. IMO-DRSA can reduce the search space using the preference information of the investor until the single most preferable solution is found. The problem is that there is no evidence on how to reduce the number of representative portfolios to minimize decision-maker’s (DM) cognitive effort during the interaction, taking the satisfaction of preferences in future distributions of portfolio components returns into account. The objective of this work was to provide a way to support the reduction of the number of representative examples presented for the investor, while regarding out-of-sample preference satisfaction of the examples that compose a data table. A simulation approach was proposed to analyze and compare methods that select a small and robust sample of representative solutions to compose data tables." @default.
- W3192376399 created "2021-08-16" @default.
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- W3192376399 date "2021-06-28" @default.
- W3192376399 modified "2023-09-30" @default.
- W3192376399 title "A Simulated IMO-DRSA Approach for Cognitive Reduction in Multiobjective Financial Portfolio Interactive Optimization" @default.
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- W3192376399 doi "https://doi.org/10.1109/cec45853.2021.9504906" @default.
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