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 evolutioanry algorithms. Various approaches and applications of evolutioanry computation to combinatorial optimization problems are introduced.
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.
C/C++ programming, basic data structures and algorithms.
No specific textbook. Readers and papers will be followed.
| Week | Topic |
| 20/9 | Introduction |
| 25-27/9 | Natural evolution, Evolutionary algorithms basics |
| 1-3/10 | Evolutionary search techniques |
| 8-10/10 | Genetic algorithms, operators, selection and parameters |
| 15-17/10 | Combinatorial optimization problems and genetic algorithms, representations |
| 23-24/10 | Theoretical foundations, convergence and design considerations |
| 30-2/11 | Genetic programming |
| 7-9/11 | Genetic programming |
| 14-16/11 | Parallel genetic algorithms |
| 21-23/11 | Mid-term (15th) |
| 28-30/11 | Other approaches and case studies |
| 5/12-7/12 | Other approaches and case studies |
| 12-14/12 | Student project presentations |
| 19-21/12 | Student project presentations |
| 26-28/12 | Student project presentations |
| 3/1 2008 | Review |
| Assignments | 20% |
| Mid-term | 25% |
| Project implemantation | 10% |
| Project presentation | 10% |
| Project referee/participation to other pres. | 10% |
| Project paper | 25% |