Weighted preferences in evolutionary multi-objective optimization

Tobias Friedrich

doi:10.1007/s13042-012-0083-y

Weighted preferences in evolutionary multi-objective optimization

2012-03-30

16

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AI

DOI：10.1007/s13042-012-0083-y

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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.

Keyword:

Evolutionary algorithms

Multi-objective optimization

User preferences

Weighted preferences in evolutionary multi-objective optimization

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