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 (Independent Researcher, India)
DOI: 10.4018/978-1-5225-0788-8.ch013
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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.
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Introduction

Current scenario of real life applications development is a result of utilizing intelligent techniques provided by soft computing family. The research work focuses on evolutionary computing for searching the optimum solution from global search space. Evolutionary computing provides the way of achieving optimized outcome with less complexity and high accuracy. Due to the above stated qualities, Evolutionary Computing is utilized as major computing approach in search and optimization. Evolutionary Computing has major four constituents such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP) and Evolutionary Strategies (ES). Genetic Algorithm is prime constituent of Evolutionary Computing (EC). GA provides efficient search and optimized outcome. But at the same time GA suffers from two major limitations such as lack of domain knowledge representation and handling imprecision and uncertainty. In order to deal with the stated limitations of GA, Fuzzy Logic is hybridized with GA. The chapter proposes machine learning approach using genetic-fuzzy hybridization for medical diagnostic application for heart disease identification. The current research work provides a novel design of genetic-fuzzy hybrid structure for automatic decision support. The chapter presents the design of intelligent procedure that supports genetics based rule learning which integrates linguistic knowledge representation. The chapter presents algorithm of implementing intelligent procedure along with discussion of every steps. The chapter explains different parameters of patent’s cardiac profile in order to decide whether patient is prone to heart disease or not. Supervised machine learning is implemented to incorporate medical expert’s knowledge into the system. Machine learning is made possible by utilizing the developed intelligent process using Genetic Algorithm and Fuzzy Logic. The research work is based on supervised machine learning approach in which machine is trained through expert’s knowledge once, and later on takes decision automatically every time. The outcome provided by Genetic Algorithm is mapped to get automatic decision for real life application. It presents discussion on the results achieved as prototypical implementation of evolutionary fuzzy hybrid model for medical diagnostic application. The significant advantage of the presented research work is that applications which do not have any mathematical formulation and still demands optimization can be easily solved using the designed approach.

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