Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker

The centrality of the decision maker (DM) is widely recognized in the multiple criteria decision-making community. This translates into emphasis on seamless human-computer interaction, and adaptation of the solution technique to the knowledge which is progressively acquired from the DM. This paper a...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 14; no. 5; pp. 671 - 687
Main Authors: Battiti, Roberto, Passerini, Andrea
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Delta modulation
Humans
Interactive decision making
Kernel
Machine learning
Operations research
Optimization
reactive search optimization
Studies
Support vector machines
support vector ranking
Training
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:The centrality of the decision maker (DM) is widely recognized in the multiple criteria decision-making community. This translates into emphasis on seamless human-computer interaction, and adaptation of the solution technique to the knowledge which is progressively acquired from the DM. This paper adopts the methodology of reactive search optimization (RSO) for evolutionary interactive multiobjective optimization. RSO follows to the paradigm of "learning while optimizing," through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. User judgments of couples of solutions are used to build robust incremental models of the user utility function, with the objective to reduce the cognitive burden required from the DM to identify a satisficing solution. The technique of support vector ranking is used together with a k-fold cross-validation procedure to select the best kernel for the problem at hand, during the utility function training procedure. Experimental results are presented for a series of benchmark problems.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2010.2058118