CEng 713
Evolutionary Computation
Fall 2011 Syllabus

Instructor:: Onur Tolga Şehitoğlu
Lecture Hours:: Thursday: 9:40-12:30 (A-101)
Web page:: http://www.ceng.metu.edu.tr/courses/ceng713/
Newsgroup:: news://news.ceng.metu.edu.tr:2050/metu.ceng.courses.713 (authenticated/secure)
https://cow.ceng.metu.edu.tr/News/index.php?group=metu.ceng.course.713 (authenticated)

Course description

This course offers basic knowledge about the class of evolutionary methods used in solving computer science problems. This includes genetic algorithms, evolutionary strategies, genetic programming, problem representations, genetic operations, theory of evolutionary algorithms. Various approaches and applications of evolutionary computation to combinatorial optimization problems are introduced.

Course Objectives

Evolutionary computation provides approximate solutions tp various scientific and engineering problems in polynomial time. Class of such problems include combinatorial optimization problems, problems in artificial intelligence and machine learning. This course offers in depth knowlegde about which evolutionary methods exists, which problems they can be applied, and how successful they are. Students will implement some of these algorithms and present latest achievements in the field.

Prerequisites

C/C++ programming, basic data structures and algorithms.

Textbooks/References

No specific textbook. Readers and papers will be followed.

Melanie Mithcell, "An Introduction to Genetic Algorithms (Complex Adaptive Systems)", MIT Press, 1998
Zbigniew Michalewics, "Genetic Algorithms + Data Structures = Evolution Programs", Springer Verlag, 1997.
Ed. Bäck, Fogel and Michalewicz, "Evolutionary Computation1: Basic Algorithms and Operators", 2000.

Outline

Week	Topic
29	Introduction
6 Oct	Natural evolution, Evolutionary algorithms basics
13 Oct	Evolutionary search techniques
20 Oct	Genetic algorithms, operators, selection and parameters
27 Oct	Combinatorial optimization problems and genetic algorithms, representations
3 Nov	Theoretical foundations, convergence and design considerations
10 Nov	Genetic programming
24 Nov	Genetic programming
1 Dec	Parallel genetic algorithms
7 Dec	Mid-term
14 Dec	Other approaches and case studies
22 Dec	Other approaches and case studies
29 Dec	Student project presentations
5 Jan	Student project presentations

Course Conduct

Assignments	3 programming assignments (small implementations using GA/GP libraries)
Student project	A recent conference paper and related papers will be read, the experiment will be replicated, comparative results and the survey will be reported and presented in the class.
Mid-term	In class or take home exam.
Final	Paper from student project will be evaluated as the final exam.

Grading

Assignments	20%
Mid-term	30%
Project implementation	10%
Project presentation	15%
Project paper	30%