Evolutionary algorithms represent a diverse group of optimization techniques loosely inspired by biological evolution. Their common characteristics are a population-based approach and a stochastic nature of optimization heuristics. Due to their versatility, they constitute an interesting alternative to traditional optimization algorithms, and they find their use in solutions to complex problems, such as multi-objective optimization or automated computer program design.

Evolutionary algorithms represent a diverse group of optimization techniques loosely inspired by biological evolution. Their common characteristics are a population-based approach and a stochastic nature of optimization heuristics. Due to their versatility, they constitute an interesting alternative to traditional optimization algorithms, and they find their use in solutions to complex problems, such as multi-objective optimization or automated computer program design. In our talk, we will deal with the relationship between evolutionary algorithms and machine learning.

We will demonstrate several examples of how a specialized evolutionary algorithm can search for an optimal machine learning model in various scenarios, and how it can supersede a human expert by an efficient search algorithm. We will focus on the areas of neuroevolution, which utilizes evolutionary computing to train neural networks, the evolutionary reinforcement learning of agents, and meta-learning, where evolutionary algorithms search the space of hyper-parameters or design complex combinations of models, the so-called workflows. We will show several original results aiming towards the silver bullet of the meta-learning algorithms - automated design of complex data mining systems tailored to given data.

Roman Neruda is with the Institute of Computer Science of the Czech Academy of Sciences (ICS CAS), Department of machine learning, where he is working in the areas of neurocomputing, evolutionary algorithms, and meta-learning. He graduated from the Faculty of Mathematics and Physics, Charles University, and obtained his CSc degree from the ICS CAS. In 1995-1996 he was with the Los Alamos National Laboratory, he worked on a joint project with colleagues from Carnegie-Mellon University, Koblenz Universitaet, University of California Chico, University of St. Etienne, and Universidad Distrital Bogota. He is the co-author of more than a hundred international publications. He teaches evolutionary algorithms and multi-agent systems at the Faculty of Mathematics and Physics, Charles University.

Its **program** consists of a **one-hour lecture** followed by a **discussion**. The lecture is based on an (internationally) exceptional or remarkable achievement of the lecturer, presented in a way which is comprehensible and interesting to a broad computer science community. The lectures are in English.

**The seminar** is organized by the organizational committee consisting of Roman Barták (Charles University, Faculty of Mathematics and Physics), Jaroslav Hlinka (Czech Academy of Sciences, Computer Science Institute), Michal Chytil, Pavel Kordík (CTU in Prague, Faculty of Information Technologies), Michal Koucký (Charles University, Faculty of Mathematics and Physics), Jan Kybic (CTU in Prague, Faculty of Electrical Engineering), Michal Pěchouček (CTU in Prague, Faculty of Electrical Engineering), Jiří Sgall (Charles University, Faculty of Mathematics and Physics), Vojtěch Svátek (University of Economics, Faculty of Informatics and Statistics), Michal Šorel (Czech Academy of Sciences, Institute of Information Theory and Automation), Tomáš Werner (CTU in Prague, Faculty of Electrical Engineering), and Filip Železný (CTU in Prague, Faculty of Electrical Engineering)

**The idea to organize this seminar** emerged in discussions of the representatives of several research institutes on how to avoid the undesired fragmentation of the Czech computer science community.