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Evolving Rules Using Genetic Fuzzy Approach - an Educational Case Study

ISSN: 2229 - 6735
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Kunjal Mankad, Priti Srinivas Sajja and Rajendra Akerkar, International Journal on Soft Computing
Dibuat : 2012-06-26, dengan 1 file

Keyword : Information and Communication Technology (ICT), Genetic Algorithms, Fuzzy Logic, Genetic Fuzzy Systems, Rule Base, Theory of Multiple Intelligence
Subjek : Evolving Rules Using Genetic Fuzzy Approach - an Educational Case Study
Url : http://airccse.org/journal/ijsc/papers/2111ijsc04.pdf
Sumber pengambilan dokumen : Internet

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy




approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a




framework to evolve rules automatically. The objective of the framework is to reduce the development




effort and guide the development procedure in user friendly fashion. The architecture presents a novel




approach which integrates fuzzy logic with system design as well as back end of the framework is used to




evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed




framework, a system to measure multiple intelligence is selected. The paper describes the detail




methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve




fuzzy rules for the selected system. The initial population of rules, evolved generations and output results




are also described. The paper concludes with the scope and applications of the work to other domains.

Deskripsi Alternatif :

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy




approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a




framework to evolve rules automatically. The objective of the framework is to reduce the development




effort and guide the development procedure in user friendly fashion. The architecture presents a novel




approach which integrates fuzzy logic with system design as well as back end of the framework is used to




evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed




framework, a system to measure multiple intelligence is selected. The paper describes the detail




methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve




fuzzy rules for the selected system. The initial population of rules, evolved generations and output results




are also described. The paper concludes with the scope and applications of the work to other domains.

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