Machine Learning: Evolutionary Algorithms
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Evolutionary Algorithms are randomized optimization methods. They are inspired by principles of biological evolution, however, applied in a technical context for the solution of mathematical or technical optimization problems,
These populationbased methods apply the principles of inheritance, variation, and the "survival of the fittest" to candidate solutions. The resulting search heuristics are applicable to a wide variety of application problems. They are conceptually relatively simple and often easy to implement. Their analysis requires elaborate tools and quickly becomes intractable. Evolutionary search is often applied to the approximate solution of hard optimization tasks for which efficient problemspecific solvers are not available.
The course starts out with a basic model of an evolutionary algorithm. Departing from this model students will learn about various aspects of evolutionary optimization on discrete and continuous search spaces, from which a systematic taxonomy of modular components will be developed.
The course applies the flipped classroom format. Students work through the relevant lecture material at home; most of the material is provided in the form of videos. All lecture material is in English. The presence time is dedicated to a practical session. Most of the practical session is filled with exercises, many of which involve programming tasks. We will use the Python programming language. All exercises are distributed in the form of Jupyter notebooks.
Lecturers
Prof. Dr. Tobias GlasmachersLecturer 
(+49) 2343225558 tobias.glasmachers@ini.rub.de NB 3/27 
Details
 Course type
 Lectures
 Credits
 6 CP
 Term
 Winter Term 2021/2022
 ELearning
 moodle course available
Dates
 Lecture

Takes place
every week on Monday from 10:15 to 13:15.
First appointment is on 11.10.2021
Last appointment is on 31.01.2022
Requirements
The course is designed for Master students of the Angewandte Informatik and Medizinphysik programs, but all students with a mathematical or technical background, e.g., studying natural science or engineering topics, should have the necessary background.
Participants must be familiar with linear algebra and elementary probability theory. For example, students should be well acquainted with the following terms:
 vector, basis, linear function, linear map, matrix
 norm, inner product, orthogonal
 probability, distribution, density, quantile
 normal distribution, expectation, variance, covariance
More advanced mathematical material will be introduced as needed.
The course is taught in a flipped classroom format. Most of the presence time is dedicated to practical exercises, many of which involve programming. The course it taught online via zoom.
The Institut für Neuroinformatik (INI) is a central research unit of the RuhrUniversität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D44801 Bochum, Germany
Tel: (+49) 234 3228967
Fax: (+49) 234 3214210