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Learning Objectives

  • LO1: To introduce concepts of Bio-inspired Computing and its applications
  • LO2: To provide insight on Artificial Neural Networks
  • LO3: To introduce Fuzzy logic and Fuzzy Systems
  • LO4: To provide knowledge on optimization algorithms

Course Outcomes

  • CO1: Ability to understand the principles of bio-inspired algorithms
  • CO2: Ability to apply bio-inspired techniques for pattern recognition and optimization tasks
  • CO3: Ability to analyse problems in medical applications using bio-inspired approaches
  • CO4: Ability to evaluate performance of optimization algorithms

Course Contents

Neural networks – Artificial neurons – Activation functions – Learning rules – Supervised and Unsupervised Learning – Single layer and multilayer perceptron – Kohenen’s self-organizing networks – Hopfield networks. Fuzzy systems – Fuzzy sets and relations – Membership functions – Rule base reduction methods – Decision making with fuzzy information – Fuzzy classification and pattern recognition – Neuro-fuzzy systems. Introduction to genetic algorithms – Parent selection – Crossover – Mutation – Genetic Programming – Particle Swarm Optimization – Ant colony optimization – Artificial immune systems – Case Studies – Fuzzy region growing for segmentation of calcifications in mammograms – Classification of normal and ectopic beats using neural networks – Image registration using hybrid bio-inspired approaches.


  1. Fausett, Laurene V. Fundamentals of neural networks: architectures, algorithms and applications, Pearson Education India, 2006.
  2. Ross, Timothy J. Fuzzy logic with engineering applications. Vol. 2. New York: Wiley, 2004.
  3. Goldberg, David E. Genetic algorithms, Pearson Education India, 2006.

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