Computational Intelligence

Syllabus of the course

I. Course Outlines

1. Introduction to CI and Soft Computing - History, State of the Art, Trend and Applications.

2. Evolutionary Algorithm

  • Background and Overview
  • Computation Models
  • Representation
  • Fast Evolutionary Programming and Evolutionary Strategy
  • Population-Oriented Simulated Annealing
  • Parallel GA and Multipopulation GA
  • Selection
  • Search Operators
  • Fitness Evaluation
  • Population Structure
  • Coevolution
  • Related Papers
  • Ant Colony and Swarm Intelligence
  • Mobile Robot Applications [Evolutionary Robotics] and Intelligent Control - Students

3. Fuzzy Logic and Systems

  • Fuzzy Sets and Membership Functions
  • Fuzzy Relation
  • Fuzzy Rules and Reasoning
  • Fuzzy Inference Systems
  • Fuzzy Control
  • MATLAB ToolBox

4. Fuzzy-Evolutionary System

  • Fundamentals
  • Mobile Robot Applications - Students
  • Classifier Systems - Students

5. Neuro-Fuzzy System

  • Fundamentals
  • Mobile Robot Applications - Students

6. Reinforcement Learning

  • Multi-Agent Robotics - Students

7. Course Review

II. Grading policy

  • Homework – Computer Simulation : Best student will present his work. 100
  • Computer Project (Group/Individual) – Robotics/Own interest Appl. 200
  • Class Participation – Question Activity/Quiz 50
  • Midterm, Final Exams 150