Department of Information and Library Science, Tamkang University Full-Time Faculty

:::

Seminar TitleModulus genetic algorithm and its appliction to fuzzy system optimization

Year87

Semester2

Published date1999-07-10

Seminar NameModulus genetic algorithm and its appliction to fuzzy system optimization

Seminar Name Other模數遺傳演算法及其在模糊系統最佳化之應用

All AuthorLin, Sinn-cheng

The Unit Of The Conference淡江大學資訊與圖書館學系

PublisherUniversity of British Columbia

Meeting NameIntelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on

Meeting PlaceHonolulu, United States

SummaryThe conventional genetic algorithm encodes the searched parameters as binary strings. After applying the basic genetic operators such as reproduction, crossover and mutation, a decoding procedure is used to convert the binary strings to the original parameter space. As the result, such an encoding/decoding procedure leads to considerable numeric errors. This paper proposes a new algorithm called modulus genetic algorithm (MGA) that uses the modulus operation to resolve this problem. In the MGA, the encoding/decoding procedure is not necessary. It has the following advantages: 1) the evolution can be speeded up; 2) the numeric truncation error can be avoided; 3) the precision of solution can be increased. The proposed MGA is applied to resolve the key problem of fuzzy inference systems-rule acquisition. The fuzzy system with MGA as learning mechanism forms an ?ntelligent fuzzy system?? Based on the proposed approach, the fuzzy rule base can be self-extracted and optimized

Keyword

Use LangEnglish

Included in

Nature Of The Meeting國際

On-campus Seminar Location

Seminar Time19990710~19990715

Corresponding Author

Country美國

Open Call for Papers

Publication style

ProvenanceIntelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on vol.1, pp.669-674

Contact Information

Maintenance Information