Course Details
Subject {L-T-P / C} : EC6610 : Evolutionary Computing Techniques { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Upendra Kumar Sahoo
Syllabus
Genetic Algorithm : Basic concepts, Search space, working principle. Encoding : binary, Octal, Hexadecimal, permutation, Value and Tree. Decoding, fitness function, Selection : Roulette-wheel, Boltzmann, Tournament, Rank and Steady-state. Elitism, Crossover : single-pint, two-point, multi-point, uniform, matrix and cross over rate, Mutation : mutation, mutation rate. Variations of GA: Adaptive GA and Real coded GA. Ant colony optimization: Ant foraging behavior, combinatorial optimization, Routing in communication network, traveling sales man problem, graph portioning, nest building. Particle swarm Optimization : basic principle, algorithm, 234 www.nitrkl.ac.in DEPARTMENT OF ELECTRICAL ENGINEERING flowchart. Variations of PSO: weighted, repulsive, stretched, comprehensive learning, combined effect PSO and clonal PSO. Bacterial Foraging Optimization: Forging theory, social foraging, foraging behaviour of E. coli bacteria, BFO algorithm, chemotatic, swarming, reproduction and elimination and dispersal. Variations of BFO: fuzzy BFO and Adaptive BFO. Artificial Immune System: overview, central and peripheral immune systems, immune network: clonal selection and its mathematical modeling, beyond clonal selection, danger theory, negative selection. Applications: function optimization, adaptive system identification, channel equalization and financial forecasting.
Course Objectives
- It helps in learning a PG student about derivative free optimization problems and their application in various engineering problems.
Course Outcomes
A PG student can study the derivative free optimization problems and their application in various engineering problems.
Essential Reading
- D. E. Goldberg, Genetic Algorithms in search, Optimization and machine learning, Addison-Wesley Professional 1 edition
- E. Bonabeau, M. Dorigo and G. Theraulaz,, Swarm Intelligence : From natural to Artificial Systems, OUP USA
Supplementary Reading
- . R. C. Eberhart, Y. Sai and J. Kennedy, Swarm Intelligence, Morgan Kaufmann 1st edition
- K. M. Passino, Biomimicry for optimization, control and automation, Springer 2005 edition