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Weighted preferences in evolutionary multi-objective optimization.
| Content Provider | CiteSeerX |
|---|---|
| Author | Friedrich, Tobias Kroeger, Trent Neumann, Frank |
| Abstract | Abstract. Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Incorporating preference information into the search of evolutionary algorithms for multi-objective optimization is of great importance as it allows one to focus on interesting regions in the objective space. Zitzler et al. have shown how to use a weight distribution function on the objective space to incorporate preference information into hypervolume-based algorithms. We show that this weighted information can easily be used in other popular EMO algorithms as well. Our results for NSGA-II and SPEA2 show that this yields similar results to the hypervolume approach and requires less computational effort. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Weighted Preference Evolutionary Multi-objective Optimization Objective Space Preference Information Evolutionary Algorithm Computational Effort Hypervolume Approach Interesting Region Popular Emo Algorithm Great Importance Multi-objective Optimization Problem Hypervolume-based Algorithm Weight Distribution Function Yield Similar Result Weighted Information Multi-objective Optimization |
| Content Type | Text |
| Resource Type | Article |
