OPTIMIZATION AND COMPUTATIONAL INTELLIGENCE (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Modelling of the Optimization Problem; Apply the tools and techniques of optimization problem and Computational Intelligence; Appraise the Computational intelligence techniques; Analyse and differentiate the Computational Intelligence domain; Propose Fuzzy Logic model and its implementation.

Topics:

1. Introduction to Optimization;
2. Simplex Method & Duality Theory;
3. Integer Programming;
4. Non Linear Programming;
5. Decision Analysis;
6. Introduction to Computational Intelligence, Genetic Algorithm and Ant Colony Optimization; Swarm Intelligence;
7. Artificial Neural Networks;
8. Fuzzy Logic Systems and Adaptive Neuro Fuzzy Inference Systems;
9. Enrichment Activity: Case Study and Tutorial in Optimization Topics: Case study of Linier/Nonlinear/Integer Programming and Decision Analysis;
10. Enrichment Activity (Lab): Case Study and Tutorial in Computational Intelligence;
11. Enrichment Activity: GUEST LECTURE The need of Optimization in the modern era;
12. Enrichment Activit (Lab): Solving Clustering Problem Rapid Miner, Phyton;
13. Enrichment Activity: Presentation  Project Presentation;
14. Enrichment Activity: GUEST LECTURE Topics: The need of Computational Intelligence in the modern era; Thesis Consultation.