this will give you general idea about the optimization technique - Genetic algorithm Editors:- Mithun Kuniyil, Mtech Technology Management
Genetic algorithms are stochastic optimization methods inspired by natural evolution and genetics. Over the last few decades, genetic algorithms have been successfully applied to many problems of b...
Genetic Algorithm for Engineering Applications with MatlabFull description
Data imputing uses to posit missing data values, as missing data have a negative effect on the computation validity of models. This study develops a genetic algorithm GA to optimize imputing for missing cost data of fans used in road tunnels by the S
famous algorithms by cormen
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cloud computingFull description
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A Brief Description on Genetic Algorithms and its application in Chemical Engineering
Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets (where variables may take on true or …Full description
Sift computing
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Soft Computing lab manual for computer science students.
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WORTH A READ WHEN YOU'RE STUDYING SOFTCOMPUTING OR ARTIFICIAL INTELLIGENCEFull description
INTRODUCTION INTRODUCTI ON TO SOFT COMPUTING NE N E U R O - F U Z Z Y A N D G E N E T I C A L G O R IT H M S
Samir Roy Associate Professor Department of Computer Science and Engineering National Institute of Technical Teachers’ Training and Research Research Kolkata Udit Chakraborty Associate Professor Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Rangpo
Delhi • Chennai
CONTENTS Preface Acknowledgements About the Authors 1. Introduction 1.1 What is is Soft Comp Computing? uting? 1.2 Fuzzy Systems 1.3 Rough Sets 1.4 Artificial Neural Networks 1.5 Evolutionary Search Strategies Chapter Summary Test Your Knowledge Answers Exercises Bibliography and Historical Notes 2. Fuzzy Sets 2.1 Crisp Sets: A Review 2.1.1 Basic Concepts 2.1.2 Operations on Sets 2.1.3 Properties of Sets 2.2 Fuzzy Sets 2.2.1 Fuzziness/Vagueness/Inexactness 2.2.2 Set Membership 2.2.3 Fuzzy Sets 2.2.4 Fuzzyness vs. Probability Probability 2.2.5 Features of Fuzzy Sets 2.3 Fuzzy Membership Functions 2.3.1 Some Popular Fuzzy Membership Functions 2.3.2 Transformations 2.3.3 Linguistic Variables 2.4 Operations on Fuzzy Fuzzy Sets 2.5 Fuzzy Relations 2.5.1 Crisp Relations 2.5.2 Fuzzy Relations 2.5.3 Operations on Fuzzy Fuzzy Relations 2.6 Fuzzy Extension Principle 2.6.1 Preliminaries 2.6.2 The Extension Principle Chapter Summary Solved Problems Test Your Knowledge Answers Exercises Bibliography and Historical Notes 3. Fuzzy Logic 3.1 Crisp Logic: A Review Review 3.1.1 Propositional Logic
3.1.2 Predicate Logic 3.1.3 Rules of Inference 3.2 Fuzzy Logic Basics 3.2.1 Fuzzy Truth Values 3.3 Fuzzy Truth in Terms of Fuzzy Sets 3.4 Fuzzy Rules 3.4.1 Fuzzy If-Then 3.4.2 Fuzzy If-Then-Else 3.5 Fuzzy Reasoning 3.5.1 Fuzzy Quantifiers 3.5.2 Generalized Modus Ponens 3.5.3 Generalized Modus Tollens Chapter Summary
