After completing the course, students have:
- a thorough knowledge of the concepts, techniques, analyses, and algorithms in the field of evolutionary computation and meta-heuristic search algorithms.
- theoretical knowledge to understand the behaviour of evolutionary and meta-heuristic search algorithms.
- a thorough knowledge of state-of-the-art applications of evolutionary computation and meta-heuristic search algorithms.
- a thorough knowledge of solving multi-objective optimization problems with metaheuristic search algorithms.
and students are capable of:
- designing efficient and high performance meta-heuristic search problem for diverse discrete optimization problems.
- reading and understanding key journal publications in the field of evolutionary computation and meta-heuristic search algorithms.
- experimentally comparing different meta-heuristic search algorithms on a set of benchmark problems.
- implementing meta-heuristic search algorithms to solve hard, discrete optimization problems.
- analyzing the performance and sensitivity of meta-heuristic search algorithms.
- performing a statistically sound analysis of the experimental results of different meta-heuristic search algorithms.
- working together with other students on designing, building, and testing evolutionary and meta-heuristic search algorithms.
- making English language presentations in writing of one’s own research.
- making English language presentations orally of one’s own research.
Assessment
Written exam (60% of final grade), report of the practical assignment (30% of final grade), seminar presentation (10% of final grade).
A repair test requires at least a 4 for the original test.
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Evolutionary algorithms are population-based, stochastic search algorithms based on the mechanisms of natural evolution.
We will study how to design representations and variation operators for specific problems. We also analyse convergence behavior and population sizing.
We will discuss how to combine EAs with local search heuristics to solve combinatorial optimization problems like graph bipartitioning, graph coloring, bin packing.
Course form
Lectures, practical assignment, seminar.
Literature
Lecture slides and papers.
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