Outline of the course
Evolutionary Computation is an approach to computation that emphasize on
a general-purpose search algorithm that use principles inspired by population
genetics to evolve solutions to problems. Two most well-known methods
are Genetic Algorithm (GA) and Genetic Programming (GP). Genetic
programming is a machine learning technique derives from genetic algorithms.
GA and GP has become increasingly popular in recent years as a method
for solving complex search problems in a large number of disciplines.
This course will illustrate the basic concept of GA/GP and their
current applications. The topics include Evolutionary Strategies (ES),
the method of real-value optimization, and Classifier System (CFS), one
of the most advanced study in Complex Adaptive System (CAS).
The content of the course will be a mixture of mathematical material
and up-to-date research materials. The lecture will be mainly the foundation
of the subject. Students are expected to do a number of self-study
on to answer the questions posted weekly. Those questions concern
the subject at a deeper level. The discussion will be held weekly
for a period of about one hour.
Accessment is 50% on written report and 50% on final exam. (3 hour written
exam).