MCA

Type: Elective
Contact Hours: 4 hours/week
Examination Duration: 3 Hours
Mode: Lecture
External Maximum Marks: 75
External Pass Marks: 30(i.e. 40%)
Internal Maximum Marks: 25
Total Maximum Marks: 100
Total Pass Marks: 40(i.e. 40%)

Instructions to paper setter for End semester examination:
Total number of questions shall be nine. Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: Introduce fundamental soft computing concepts with an exposure to non-traditional techniques for problem solving and optimization. Provide Soft Computing based research oriented direction for solving imprecisely defined problems. Provide a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing.

Course Outcomes:  At the end of this course, the student will be able to:
MCA-20-44(i).1        have a knowledge of soft computing techniques along with their applications and non-traditional metaheuristic optimization and data clustering techniques & algorithms for obtaining optimized solutions to optimization, computational intelligence, and design/scheduling applications;
MCA-20-44(i).2        apply fuzzy logic theory to imprecisely defined problems;
MCA-20-44(i).3        use Neural Networks concepts to find solutions to problems where normally algorithmic methods do not exist or are costly;
MCA-20-44(i).4         design high-quality solutions using Genetic Algorithms for optimization and search problems and have exposure to MATLAB environment for implementing solutions to problems using soft computing techniques.

                                                                                     Unit – I
Soft Computing : Conventional AI to Computational Intelligence; Soft Computing Constituents and Applications.
Introduction to Non-traditional Metaheuristic Optimization Techniques: Random Optimization, Simulated Annealing, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization, Harmony Search, Memetic Algorithms, Other Evolutionary Algorithms such as Firefly Algorithm, Bee Algorithm, Shuffled Frog Leap algorithm, Bat algorithm etc.
Data Clustering Algorithms: K-Means, Fuzzy C-Means, Mountain Clustering, Subtractive Clustering.
                                                                                    Unit – II
Fuzzy Set theory: Fuzzy Sets & Classical Sets; Operations on Fuzzy Sets, Fuzzy Relations, Linguistic Variables.
Membership Functions: Introduction, Features, & Fuzzification, Methods of Membership Value Assignment; Defuzzification.
Fuzzy Systems: Crisp Logic, Predicate Logic, Fuzzy Logic; Fuzzy Rule Base and Approximate Reasoning, Fuzzy Quantifiers; Fuzzy Inference Systems, Fuzzy Decision Making, Fuzzy Logic Control System; Fuzzy Expert Systems.
                                                                                      Unit – III
Neural Networks: Fundamental Concepts, Basic Models and Architecture; Machine Learning Using Neural Networks; Associative Memory Networks and their Applications.
Supervised Learning Neural Networks: Perceptron Networks, Radial Basis Function Networks: Back Propagation Neural Network: Architecture, Learning, Applications, & Research Directions; The Boltzman Machine.
Unsupervised Learning Networks: Competitive Learning networks; Kohonen Self-Organizing Networks; Hebbian learning; The Hopfield Network; Counter propagation Networks; Adaptive Resonance Theory: Introduction, Architecture, & Applications; Feed forward Networks; Reinforcement Learning.
                                                                                      Unit – IV
Genetic Algorithms: Introduction to Genetic Algorithms (GA) and their Terminology; Traditional Optimization and Search Techniques vs. Genetic Algorithm ; Operators in Genetic Algorithms; Problem Solving using Genetic Algorithm; Classification of Genetic Algorithms; Holland’s Classifier Systems; Genetic Programming; Advantages and Limitations of Genetic Algorithm; Applications of Genetic Algorithm; Applications of GA in Machine Learning.
Introduction to Hybrid Systems; MATLAB Environment for Soft Computing Techniques.
Text Books:
⦁ S. N. Sivanandam & S. N. Deepa, Principles of Soft Computing, Wiley – India.
⦁ Jyh Shing Roger Jang, Chuen Tsai Sun, Eiji Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall.
Reference Books:
⦁ S.Rajasekaran and G.A.Vijayalakshmi Pai, Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications, Prentice-Hall of India Pvt. Ltd.
⦁ George J. Klir and Bo Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall.
⦁ George J. Klir, Ute St. Clair, Bo Yuan, Fuzzy Set Theory: Foundations and Applications Prentice Hall.
⦁ Simon O. Haykin, Neural Networks: a comprehensive foundation, Pearson Education.
⦁ Mitchell Melanie, An Introduction to Genetic Algorithm, Prentice Hall
⦁ Goldberg D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Pearson Education.
⦁ Ahmad Lotfi, Jonathan Garibaldi, Applications and Science in Soft Computing, Springer.
⦁ Rajkumar Roy, Mario Koppen Soft Computing and Industry: Recent Applications, Springer.
⦁ James A. Freeman, David M. Skapura, Neural Networks Algorithms, Applications, and Programming Techniques, Pearson Education India.
⦁ Du, Ke-Lin, Swamy, M. N. S., Search and Optimization by Metaheuristics: Techniques and Algorithms, Springer
⦁ Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loaiciga, Meta-heuristic and Evolutionary Algorithms for Engineering Optimization, Wiley