Syllabus Computational Intelligence - (417530) Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction To Computational Intelligence Introduction to Computational Intelligence, Paradigms of Computational Intelligence, Difference between Artificial Intelligence and Computational Intelligence, Approaches to Computational Intelligence, Synergies of Computational Intelligence Techniques, Applications of Computational Intelligence, Grand Challenges of Computational Intelligence. (Chapter - 1) Unit II Fuzzy Logic Introduction to Fuzzy Set - Introduction, definition, membership Function, Fuzzy operator, Fuzzy Set Characteristics, Fuzziness and Probability. Fuzzy Logic and Reasoning - Fuzzy Logic: Linguistics Variables and Hedges, Fuzzy Rules. Fuzzy Inferencing : neuro inferencing Fuzzification, Defuzzification Fuzzy logic Controllers : Fuzzy logic Controllers, Fuzzy logic Controller Types. (Chapter - 2) Unit III Evolutionary Computing Introduction, Evolutionary Computing, Terminologies of Evolutionary Computing, Genetic Operators, Evolutionary Algorithms: - Genetic Algorithm, Evolution Strategies, Evolutionary Programming, Genetic Programming, Performance Measures of EA, Evolutionary Computation versus Classical Optimization. Advanced Topics : Constraint Handling, Multi-objective Optimization, Dynamic Environments Swarm Intelligence : Ant Colony Optimization. (Chapter - 3) Unit IV Genetic Algorithm Introduction to Basic Terminologies in Genetic Algorithm : Individuals, Population, Search space, Genes, Fitness function, Chromosome, Trait, Allele, Genotype and Phenotype. GA Requirements and representation - Binary Representations, Floating-Point Representations Operators in Genetic Algorithm : Initialization, Selection, Crossover (Recombination), Mutation; fitness score, Stopping Condition, reproduction for GA Flow, Constraints in Genetic Algorithms. Genetic Algorithm Variants : Canonical Genetic Algorithm (Holland Classifier System), Messy Genetic Algorithms, Applications, and benefits of Genetic Algorithms. (Chapter - 4) Unit V Computational Intelligence and NLP Introduction, Word embedding Techniques-Bag of Words, TF-IDF,Word2Vec, Glove, Neural word embedding, Neural Machine Translation, Seq2Seq and Neural Machine Translation, translation Metrics (BLEU Score & BERT Score) , Traditional Versus Neural Metrics for Machine Translation Evaluation, Neural Style Transfer, Pertained NLP BERT Model and its application. (Chapter - 5) Unit VI Artificial Immune Systems Natural Immune System, Artificial Immune Models, Artificial Immune System Algorithm, Classical View Models, Clonal Selection Theory Model, Network Theory Model, Danger Theory Model, Dendritic cell Model, Applications of AIS models. (Chapter - 6)
