Welcome to the InfoSci Platform
Reference Hub3
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic

An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic

Kunjal Bharatkumar Mankad
Copyright: © 2017 |Pages: 37
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch010
Cite Chapter Cite Chapter

MLA

Mankad, Kunjal Bharatkumar. "An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 245-281. https://doi.org/10.4018/978-1-5225-1759-7.ch010

APA

Mankad, K. B. (2017). An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 245-281). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch010

Chicago

Mankad, Kunjal Bharatkumar. "An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 245-281. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch010

Export Reference

Mendeley
Favorite

Abstract

Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.