Multi-Objective Genetic Programming Based Design of Fuzzy Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Freischlad:2005:ICCCE,

• author = "M. Freischlad and M. Schnellenbach-Held",
• title = "Multi-Objective Genetic Programming Based Design of Fuzzy Systems",
• booktitle = "Proceedings of the 2005 ASCE International Conference on Computing in Civil Engineering",
• year = "2005",
• editor = "Lucio Soibelman and Feniosky Pena-Mora",
• address = "Cancun, Mexico",
• month = jul # " 12-15",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1061/40794(179)62",
• abstract = "The Multi-Objective Domain Knowledge Augmented Genetic Fuzzy System (MODA-GFS) is a GP based fuzzy system for the data-driven generation of fuzzy rule based systems. The algorithm incorporates domain specific knowledge that is used by human knowledge engineers in the manual fuzzy system design process. The combination of characteristics of two individuals is most interesting if both individuals complement each other. In terms of fuzzy systems this means a potential crossover partner (parent B) has a lower approximation error in an area of the input space, where parent A has a higher error. Within MODA-GFS a method for the determination of feasible crossover mates is implemented. In addition MODA-GFS includes a method for the goal-oriented selection of parent rules that are handed down to the offspring. Especially in the domain of knowledge representation the quality of a fuzzy system is not only determined by its approximation capability but also by its transparency. In order to assure the automated generation of fuzzy systems that are both accurate and transparent multi-objective optimisation methods are implemented. Tests carried out on test functions as well as on real world data sets have shown that the incorporation of domain knowledge significantly speeds up the evolution process. Besides these test results the integration and application of the new methods for automated generation of fuzzy models within a learning expert system environment are described in this paper. Finally an outlook on the current and future work is given, i.e. the transfer of the presented findings to the evolutionary optimisation of large-scale structures.",
• notes = "c2005 ASCE",

}

Genetic Programming entries for Mark Freischlad Martina Schnellenbach-Held

Citations