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Cursus: INFOEA
INFOEA
Evolutionary computing
Cursus informatie
CursuscodeINFOEA
Studiepunten (EC)7,5
Cursusdoelen
After completing the course, student 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 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
The assessment consists of a written exam (50% of the final mark), labs (40%), and paper report (10%).
Inhoud
Evolutionary algorithms are population-based, stochastic search algorithms based on the mechanisms of natural evolution. This course covers how to design representations and variation operators for specific problems. Furthermore convergence behavior and population sizing are analysed. The course focuses on the combination of evolutionary algorithms with local search heuristics to solve combinatorial optimization problems like graph bipartitioning, graph coloring, and bin packing.
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