An Easy Introduction to Theory of Evolutionary Computation
Benjamin Doerr (École Polytechnique)
The theory of evolutionary computation has always accompagnied the design and analysis of evolutionary algorithms. It has described their working principles, given advice on how to set their parameters, and has even proposed new algorithms. Due to their abstract nature, researcher from outside the theory domain often find it hard to understand and profit from these results. This tutorial addresses all CEC attendees who do not regularly use theoretical methods in their research and gives them a smooth introduction to the theory of evolutionary computation. Complementing other theory tutorials, we do not discuss mathematical methods or particular results, but explain
Benjamin Doerr is a full professor at the French Ecole Polytechnique. He received his diploma (1998), PhD (2000) and habilitation (2005) in mathematics from Kiel University. His research area is the theory of both problem-specific algorithms and randomized search heuristics like evolutionary algorithms. Major contributions to the latter include runtime analyses for existing evolutionary algorithms, the determination of optimal parameter values, and the theory-guided design of novel operators, on-the-fly parameter choices, and whole new evolutionary algorithms.
Together with Frank Neumann and Ingo Wegener, Benjamin Doerr founded the theory track at GECCO and served as its co-chair 2007-2009 and 2014. He is a member of the editorial boards of Artificial Intelligence, Evolutionary Computation, Natural Computing, Theoretical Computer Science, and three journal on classic algorithms theory. Together with Anne Auger, he edited the book Theory of Randomized Search Heuristics (World Scientific 2011). Together with Frank Neumann, he is an editor of the book Theory of Evolutionary Computation - Recent Developments in Discrete Optimization (Springer 2020).