The Impact of Genetic Fuzzy Modeling for Machine Intelligence

Machine intelligence is one of the crucial areas of intelligent system design. Computational intelligence has provided smart techniques for knowledge engineering, learning, searching, classification, etc. The paper outlines various methods of classification modeling. The paper shows role of predictive classifier for knowledge-based system. Evolutionary computing is discussed as one of the constituent of predictive classifier. The paper justifies the importance of evolutionary computing for machine learning. Soft Computing methods are proven very successful in the area of machine learning. The constituents of soft computing such as Genetic Algorithm, Fuzzy Logic, Probabilistic Reasoning, and Neural Networks have provided numerous advantages in order to handle real life applications. Soft Computing methods are widely popular for providing hybrid methods such as Fuzzy-Genetic, Neural –Fuzzy, Genetic-Neural-Fuzzy, etc. Hybridization of Genetic Algorithm and Fuzzy Logic provides major advantages for machine learning. The paper focuses on genetic- fuzzy hybrid model. It also narrates three major types of hybrid models of Genetic-Fuzzy integration; i.e. the Michigan, the Pittsburg and the Iterative Rule Learning. As a result of hybridization of genetic algorithm with fuzzy logic, several intelligent decision support systems are developed. The literature survey includes several applications based on Genetic-Fuzzy hybridization. The final section concludes with significance of Genetic- Fuzzy modeling for machine learning.

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