Evolutionary computation (EC) is an umbrella term for a family of stochastic optimisation algorithms that take inspiration from Darwinian evolution theory. In a nutshell (and very generic) such algorithms maintain a multi-set/population of solution candidates for the optimisation problem at hand. This population evolves in an iterative manner by applying bio-inspired variation and survival-selection operators until some predefined termination criterion is met. Evolutionary algorithms (EAs) are general purpose solvers and have proven to perform exceptionally well in many (black-box) real-world applications, in the field of multi-objective optimisation. (Problem-tailored) EAs are also very successful in combinatorial optimisation. A great example for the latter is the Edge-Assembly-Crossover algorithm (EAX) for the well-known NP-hard Euclidean Travelling Salesperson Problem (TSP). EAX manages to find (close to) optimal solutions for TSP instances with thousands of notes within minutes or even seconds.
This block seminar course will be held in English, towards the end of the 2022/2023 winter semester. Enrolment is restricted to at most 20 Bachelor or Master students, preferably with a background in optimisation. Students will work in groups of two on different EC-related topics, including but not limited to evolutionary optimisation in the continuous and discrete domain, genetic programming, evolutionary multi-objective optimisation, principled performance assessment, and more recent EC-branches like evolutionary diversity optimisation~(EDO) and quality diversity~(QD). Each group will be assigned a paper from the research literature, which will serve as the starting point for an in-depth investigation of a specific topic; the results of this investigation will be presented in class and compiled into a report.
Preferably students should have a background in optimisation.
Registration to the seminar is handled via the SuPra system.
Dr. Jakob Bossek
Assistant Professor (Akademischer Rat)
E-mail: bossek[at]aim[dot]rwth-aachen[dot]de
Phone: +49 241 80 21453
