After completing the course, students:
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Have a thorough knowledge of the concepts, techniques, analyses, and algorithms in the field of evolutionary computation and meta-heuristic search algorithms.
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Have theoretical knowledge to understand the behaviour of evolutionary and meta-heuristic search algorithms.
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Have a thorough knowledge of state-of-the-art applications of evolutionary computation and meta-heuristic search algorithms.
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Have a thorough knowledge of solving multi-objective optimization problems with metaheuristic search algorithms.
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Are capable of designing efficient and high performance meta-heuristic search problem for diverse discrete optimization problems.
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Are capable of reading and understanding key journal publications in the field of evolutionary computation and meta-heuristic search algorithms.
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Are capable of experimentally comparing different meta-heuristic search algorithms on a set of benchmark problems.
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Are capable of implementing meta-heuristic search algorithms to solve hard, discrete optimization problems.
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Are capable of analyzing the performance and sensitivity of meta-heuristic search algorithms.
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Are capable of performing a statistically sound analysis of the experimental results of different meta-heuristic search algorithms.
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Are capable of working together with other students on designing, building, and testing evolutionary and meta-heuristic search algorithms.
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Are capable of making English language presentations in writing of one’s own research.
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Are capable of making English language presentations orally of one’s own research.
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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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