6533b855fe1ef96bd12b1522
RESEARCH PRODUCT
An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods
Ana Belen RuizBekir AfsarKaisa Miettinensubject
aspiration levelsMathematical optimizationComputer sciencepäätöksenteko02 engineering and technologySpace (commercial competition)interactive methodsDecision makerMulti-objective optimizationmonitavoiteoptimointidecision makingmany-objective optimizationoptimointiRegion of interestmonimuuttujamenetelmät020204 information systemsPerformance comparison0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingperformance comparisondescription
Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phase. We demonstrate the ADM by comparing interactive versions of RVEA and NSGA-III on benchmark problems with up to 9 objectives. The experiments show that our ADM is efficient and allows repetitive testing to compare interactive evolutionary methods in a meaningful way. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |