MC4014 Bio Inspired Computing Syllabus:
MC4014 Bio Inspired Computing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To Learn bio-inspired theorem and algorithms
To Understand random walk and simulated annealing
To Learn genetic algorithm and differential evolution
To Learn swarm optimization and ant colony for feature selection
To understand bio-inspired application in various fields
UNIT I INTRODUCTION
Introduction to algorithm – Newton ‘ s method – optimization algorithm – No-Free-Lunch Theorems – Nature-Inspired Metaheuristics -Analysis of Algorithms -Nature Inspired Algorithms -Parameter tuning and parameter control
UNIT II RANDOM WALK AND ANNEALING
Random variables – Isotropic random walks – Levy distribution and flights – Markov chains – step sizes and search efficiency – Modality and intermittent search strategy – importance of randomization- Eagle strategy-Annealing and Boltzmann Distribution – parameters -SA algorithm – Stochastic Tunneling
UNIT III GENETIC ALGORITHMS AND DIFFERENTIAL EVOLUTION
Introduction to genetic algorithms and – role of genetic operators – choice of parameters – GA variants – schema theorem – convergence analysis – introduction to differential evolution – variants – choice of parameters – convergence analysis – implementation.
UNIT IV SWARM OPTIMIZATION AND FIREFLY ALGORITHM
Swarm intelligence – PSO algorithm – accelerated PSO – implementation – convergence analysis – binary PSO – The Firefly algorithm – algorithm analysis – implementation – variants- Ant colony optimization toward feature selection
UNIT V APPLICATIONS OF BIO INSPIRED COMPUTING
Improved Weighted Threshold Histogram Equalization Algorithm for Digital Image Contrast Enhancement Using Bat Algorithm – Ground Glass Opacity Nodules Detection and Segmentation using Snake Model – Mobile Object Tracking Using Cuckoo Search- Bio inspired algorithms in cloud computing- Wireless Sensor Networks using Bio inspired Algorithms
TOTAL: 45 PERIODS
SUGGESTED ACTIVITIES:
1. Identify problems with domains where Bio inspired computing will be most suitable to find a solution
2. Identify the applications of Random walk
3. List out the applications of Genetic algorithms in AI and machine learning
4. Apply swarm intelligence and Firefly algorithm to find an optimal solution for a problem Compare their efficiency and accuracy
5. Ty to implement a Bio inspired computing in Networks/Biomedical/Cloud computing applications to obtain an optimal solution
COURSE OUTCOMES:
Upon completion of the course, the students should be able to
CO1:Implement and apply bio-inspired algorithms
CO2:Explain random walk and simulated annealing
CO3:Implement and apply genetic algorithms
CO4:Explain swarm intelligence and ant colony for feature selection
CO5:Apply bio-inspired techniques in various fields
REFERENCES
1. Eiben,A.E.Smith, James E, “Introduction to Evolutionary Computing”, Springer 2nd Edition2015.
2. Helio J.C. Barbosa, “Ant Colony Optimization – Techniques and Applications”, IntechFirstEdition,2013
3. Xin-She Yang , Joao Paulo papa, “Bio-Inspired Computing and Applications in Image Processing”,Elsevier First Edition, 2016
4. Xin-She Yang, “Nature Inspired Optimization Algorithm”, Elsevier First Edition 2014
5. Yang ,Cui,XIao,Gandomi,Karamanoglu,”Swarm Intelligence and Bio-Inspired Computing”, Elsevier First Edition 2013