Evolutionary multi-objective optimization for evolving hierarchical fuzzy system

In this paper, a Multi-Objective Extended Genetic Programming (MOEGP) algorithm is developed to evolve the structure of the Hierarchical Flexible Beta Fuzzy System (HFBFS). The proposed algorithm allows finding the best representation of the hierarchical fuzzy system while trying to attain the desir...

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Vydáno v:IEEE transactions on evolutionary computation s. 3163 - 3170
Hlavní autoři: Jarraya, Yosra, Bouaziz, Souhir, Alimi, Adel M., Abraham, Ajith
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2015
Témata:
Accuracy
Classification algorithms
classification problems
feature selection
Fuzzy systems
Genetic programming
Hierarchical Flexible Beta Fuzzy System
hybrid Bacterial Foraging Optimization Algorithm
Multi-Objective Extended Genetic Programming algorithm
Optimization
Sociology
Statistics
ISSN:1089-778X
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Shrnutí:In this paper, a Multi-Objective Extended Genetic Programming (MOEGP) algorithm is developed to evolve the structure of the Hierarchical Flexible Beta Fuzzy System (HFBFS). The proposed algorithm allows finding the best representation of the hierarchical fuzzy system while trying to attain the desired balance of accuracy/interpretability. Furthermore, the free parameters (Beta membership functions and the consequent parts of rules) encoded in the best structure are tuned by applying the hybrid Bacterial Foraging Optimization Algorithm (the hybrid BFOA). The proposed methodology interleaves both MOEGP and the hybrid BFOA for the structure and the parameter optimization respectively until a satisfactory HFBFS is found. The performance of the approach is evaluated using several classification datasets with low and high input dimensions. Results prove the superiority of our method as compared with other existing works.
ISSN:1089-778X
DOI:10.1109/CEC.2015.7257284