Optimisation and Reasoning

This course covers major (combinatorial) solving techniques and their core algorithms, namely constraint programming, integer programming, SAT and first-order logic, and classical planning. During this course you focus on modelling practical problems, expressing them in the survey paradigms; algorithms for solving such problems are covered in the follow-up "Algorithms for Intelligent Decision Making" course. Furthermore, the course explores how data can be used to improve and accompany such models. Lastly, the course focuses on conducting proper empirical evaluations.

This course covers state-of-the-art algorithms used in practice to solve combinatorial optimisation problems, focussing on (Max)SAT, (lazy clause generation) constraint programming, and mixed-integer linear programming. The course offers you an in-depth view of the algorithms including advanced techniques (e.g., propagator design, column generation), alongside techniques that can certify the correctness of the output of the algorithms beyond reasonable doubt. After the course, you will have a principled understanding of the algorithms and will be able to use that knowledge to design their own tailored approaches to solve combinatorial optimisation problems.

This course covers a spectrum of topics in Evolutionary Algorithms, ranging from basic concepts to advanced, recent, and state-of-the-art research, and ranging from theoretical to applied. In particular, topics include genetic algorithms, evolution strategies, genetic programming, estimation-of-distribution algorithms, optimal mixing evolutionary algorithms, multi-objective optimization, and real-world applications. Evolutionary Algorithms are being used to solve real-world problems in many areas, e.g., to optimize the layout of electrical wind farms, to automatically create radiation therapy treatment plans, and to optimize the architectures of deep neural networks.