Evolutionary computation methods are problem solving techniques
that mimic the process of natural selection.  The field of
evolutionary computation has attracted considerable interest
in recent years.  The number of publications in the field is 
increasing exponentially.  There is a rapid growth of interest 
in the application of evolutionary computation methods in many 
fields, including computer science, engineering and 
bioinformatics.

The main objectives of this course are:
 -An in-depth introduction to evolutionary computation methods.
 -Exploration of research problems in evolutionary computation
  and its applications which may lead to work on a project or a
  dissertation.

The course is intended primarily for computer science and 
artificial intelligence students as well as graduate students
from engineering and biological sciences departments.
Graduate students from other departments may also find the
course interesting.

* Part I: Elements of evolutionary computation 
  - Evolutionary computation concepts 
  - Genetic algorithms 
    Representation 
    GA operators 
    GAs in optimization 
    GA based classifier systems 
  - Other evolutionary computation paradigms 
    Genetic Programming 
    Evolution strategies 
    Evolutionary programming 
    Hybrid methods
* Part II: Evolutionary computation applications 
  - Computer science and electrical engineering applications: 
    VLSI design 
    Computer architecture and circuit design 
    recognition 
  - Engineering applications: 
    Design: mechanical and aerospace, architectural ... etc.  
    Manufacturing and scheduling, 
    Control
  - Biological/bioinformatics applications
* Part III: Advanced and related topics 
  - Parallel evolutionary computation 
  - Artificial life and/or Co-evolution 
  - Simulated Annealing and other global optimization methods