I am currently working on the foundations of machine learning and the applicationof evolutionary algorithms to Real World learning problems. The search for foundations of machine learning leads to three main questions:
How should a learning system represent and process uncertain information, or, what is the proper inductive logic?
What set of possible explanations/models should the system consider?
How to balance evidence and complexity of explanations/models?
In the long run, the learning system should be able to detect all algorithmic definable regularities in a data stream.
Application Areas
Analysis of Microarray Data
Computational Finance
Planning of Mobile Radio Networks
Teaching
Summer Lecture 2008: Analysis of Microarray Data and Gene Co-Expression Networks
The exam for this course will be on Wednesday, 2008-07-23, from 10:00 to 12:00, at Rheinsaal, B-IT.
The resit will be on Wednesday, 2008-10-01, from 10:00 to 12:00, at
Marschallsaal, B-IT. (Only students who were registered for the first exam
are admitted to the resit)
IPEC Winterschool 2008: Analyzing Microarray Data with Methods from Statistics and Machine Learning
From Theory to Practice: An Evolutionary Algorithm for the Antenna
Placement Problem
S. Tsutsui, A. Ghosh (Eds.): Advances in Evolutionary Computation,
pp. 713-737, Springer, 2003
Jörg Zimmermann, Robin Höns, Heinz Mühlenbein:
ENCON: An Evolutionary Algorithm for the Antenna Placement Problem Computers & Industrial Engineering, 44(2): 209-226, 2003
Frank Schweitzer, Jörg Zimmermann, Heinz Mühlenbein:
Communication and Self-Organisation in Complex Systems: A Basic Approach
M. M. Fischer, J. Fröhlich (Eds.): Knowledge, Complexity and Innovation
Systems, pp. 275-296, Springer, 2001