# Roman Neruda presents Evolutionary Algorithms in Machine Learning

On 2019-04-25 - 2019-04-25 16:15:00 at KN:E-301 Šrámková posluchárna, FEL ČVUT, Karlovo nám. 13, Praha 2

42. Prague Computer Science Seminar

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.

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

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.

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.

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

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.

External www: http://www.praguecomputerscience.cz/?l=en&p=42