# InfoCoBuild

## Introduction to Soft Computing

Introduction to Soft Computing. Instructor: Prof. DebasisSamanta, Department of Computer Science and Engineering, IIT Kharagpur. Soft computing is an emerging approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision. Soft computing is based on some biological inspired methodologies such as genetics, evolution, ante's behaviors, particles swarming, human nervous systems, etc. Now, soft computing is the only solution when we don't have any mathematical modeling of problem solving (i.e., algorithm), need a solution to a complex problem in real time, easy to adapt with changed scenarios and can be implemented with parallel computing. It has enormous applications in many application areas such as medical diagnosis, computer vision, hand-written character recognition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc. (from nptel.ac.in)

 Lecture 22 - GA Operator: Crossover Techniques (cont.)

Go to the Course Home or watch other lectures:

 Lecture 01 - Introduction Lecture 02 - Introduction to Fuzzy Logic Lecture 03 - Fuzzy Membership Functions and Defining Membership Functions Lecture 04 - Fuzzy Operations Lecture 05 - Fuzzy Relations Lecture 06 - Fuzzy Relations (cont.), Fuzzy Propositions Lecture 07 - Fuzzy Implications Lecture 08 - Fuzzy Inferences Lecture 09 - Defuzzification Techniques Lecture 10 - Defuzzification Techniques (cont.) Lecture 11 - Fuzzy Logic Controller Lecture 12 - Fuzzy Logic Controller (cont.) Lecture 13 - Fuzzy Logic Controller (cont.) Lecture 14 - Concept of Genetic Algorithm Lecture 15 - Concept of Genetic Algorithm (cont.), GA Strategies Lecture 16 - GA Operator: Encoding Schemes Lecture 17 - GA Operator: Encoding Schemes (cont.) Lecture 18 - GA Operator: Selection Lecture 19 - GA Operator: Selection (cont.) Lecture 20 - GA Operator: Crossover Techniques Lecture 21 - GA Operator: Crossover Techniques (cont.) Lecture 22 - GA Operator: Crossover Techniques (cont.) Lecture 23 - GA Operator: Mutation and Others Lecture 24 - Multi-objective Optimization Problem Solving Lecture 25 - Multi-objective Optimization Problem Solving (cont.) Lecture 26 - Concept of Domination Lecture 27 - Non-pareto based Approaches to Solve MOOPs Lecture 28 - Non-pareto based Approaches to Solve MOOPs (cont.) Lecture 29 - Pareto based Approaches to Solve MOOPs Lecture 30 - Pareto based Approaches to Solve MOOPs (cont.) Lecture 31 - Pareto based Approaches to Solve MOOPs (cont.) Lecture 32 - Pareto based Approaches to Solve MOOPs (cont.) Lecture 33 - Pareto based Approaches to Solve MOOPs (cont.) Lecture 34 - Introduction to Artificial Neural Network Lecture 35 - ANN Architectures Lecture 36 - Training ANNs Lecture 37 - Training ANNs (cont.) Lecture 38 - Training ANNs (cont.) Lecture 39 - Training ANNs (cont.) Lecture 40 - Soft Computing Tools