Name
Title | ||
|---|---|---|
Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TEVC.2010.2058118 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
brain-computer interfaces,decision making,evolutionary computation,human computer interaction,learning (artificial intelligence),optimisation,DM,RSO,brain-computer interaction,decision making,evolutionary algorithm,human-computer interaction,k-fold cross validation procedure,multiobjective optimization,online machine learning,reactive search optimization,robust incremental models,self-tuning optimization,user utility function,Interactive decision making,machine learning,reactive search optimization,support vector ranking | Online machine learning,Satisficing,Evolutionary algorithm,Computer science,Support vector machine,Evolutionary computation,Multi-objective optimization,Artificial intelligence,Genetic algorithm,Machine learning,Reactive search optimization | Journal |
Volume | Issue | ISSN |
14 | 5 | 1089-778X |
Citations | PageRank | References |
32 | 0.94 | 38 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Battiti | 1 | 1937 | 262.40 |
| Andrea Passerini | 2 | 569 | 46.88 |