About the Course

The course examines various advanced topics in the theory and practice of evolutionary computation and other population-oriented stochastic search procedures. The course will start with a primer on evolutionary computation techniques. Then it will move onto various topics. These topics may include coevolution methods and theory, markov-chain analysis of EC, island models, fine-grained diffusion models, genetic programming, learning classifier systems, ant colony optimization, particle swarm optimization, morphological representations, multiobjective optimization, local search techniques, and artificial life. Students are encouraged to suggest further topics.

Further information will appear on the Course Web Page

Requirements

There are no textbooks for this course: readings will focus on current journal articles and conference papers, plus excerpts from texts.

Projects may be done in a variety of languages, but some library code will be available in Java.

Grading Policies

At present, the course will consist of one or two exams, a few small projects, presentations, and a final project. The breakdown will be approximately:

Projects and Presentations	20%
Final Project	30%
Final Exam	50%