6533b862fe1ef96bd12c610a
RESEARCH PRODUCT
Efficiency improvement of DC* through a Genetic Guidance
Franz RothlaufMarco LucarelliCorrado MencarCiro Castiellosubject
Exponential complexity0209 industrial biotechnologyMathematical optimizationComputationProcess (computing)02 engineering and technologyFuzzy logic020901 industrial engineering & automationGenetic algorithm0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingAlgorithmMathematicsInterpretabilityData compressiondescription
DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A∗ algorithm embodied in DC∗ with a GA solution, the time required to perform data granulation can be reduced by at least 45% and up to 99%.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-01 | 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |