Modification of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Design of Fuzzy Rule-Based Classification Systems

We examine three methods for improving the ability of evolutionary multiobjective optimization (EMO) algorithms to find a variety of fuzzy rule-based classification systems with different tradeoffs with respect to their accuracy and complexity. The accuracy of each fuzzy rule-based classification sy...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of ... IEEE International Conference on Fuzzy Systems s. 809 - 814
Hlavní autoři: Narukawa, K., Nojima, Y., Ishibuchi, H.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 2005
Témata:
Algorithm design and analysis
Design engineering
Design optimization
Evolutionary computation
Fuzzy systems
Humans
Neural networks
Optimization methods
Pattern classification
ISBN:0780391594, 9780780391598
ISSN:1098-7584
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We examine three methods for improving the ability of evolutionary multiobjective optimization (EMO) algorithms to find a variety of fuzzy rule-based classification systems with different tradeoffs with respect to their accuracy and complexity. The accuracy of each fuzzy rule-based classification system is measured by the number of correctly classified training patterns while its complexity is measured by the number of fuzzy rules and the total number of antecedent conditions. One method for improving the search ability of EMO algorithms is to remove overlapping rule sets in the three-dimensional objective space. Another method is to choose similar rule sets as parents for crossover operations. The other method is to bias the selection probability of parents toward rule sets with high accuracy. The effectiveness of each method is examined through computational experiments on benchmark data sets
ISBN:0780391594
9780780391598
ISSN:1098-7584
DOI:10.1109/FUZZY.2005.1452498