ECiP 2017: Berlin July 17th 2017
In the Evolutionary Computation in Practice (ECiP) track, well-known speakers with outstanding reputation in academia and industry present background and insider information on how to establish reliable cooperation with industrial partners. They actually run companies or are involved in cooperations between academia and industry.
If you attend, you will learn multiple ways to extend EC practice beyond the approaches found in textbooks. Experts in real-world optimization with decades of experience share their approaches to creating successful projects for real-world clients. Some of what they do is based on sound project management principles, and some is specific to our type of optimization projects.
A panel of experts describes a range of techniques you can use to identify, design, manage, and successfully complete an EA project for a client. If you are working in academia and are interested in managing industrial projects, you will receive valuable hints for your own research projects.
- S1 Bridging the gap between academia and industry: How to establish & continue cooperation with industrial partners
- S2 “Real” real-world optimization: Best practice
Talks & Discussions
- S3 Ask the experts / Getting a job: What to do and what not to do
MAGMA Gießereitechnologie GmbH
Hooman Shayani. Senior Principal Research Scientist. Autodesk, Ltd.
Daniel Hein is a research scientist at Siemens Corporate Technology in Munich, Germany, working in the area of applied machine learning and reinforcement learning. He received his B.Sc. degree in Computer Science from the University of Applied Sciences Zwickau, Germany, in 2011 and the M.Sc. degree in Informatics from the Technische Universität München, Germany, in 2014. He is currently pursuing a Ph.D. in Informatics at Technische Universität München, Germany. His main research interests include evolutionary algorithms, particle swarm optimization, genetic programming, interpretable reinforcement learning, and industrial applications of machine learning approaches.
|S-1||17.07.2017||Deb||Collaborations with industry||E. Goodman|
|S-1||17.07.2017||Nugent||Springer, TBA||T. Bartz-Beielstein|
|S-1||17.07.2017||Kokot||Integration of genetic Algorithms into a Simulationsoftware||A. Fischbach|
|S-1||17.07.2017||Shayani||Autodesk, TBA||T. Bartz-Beielstein|
|S-2||17.07.2017||Hein||Particle Swarm Optimization Policy (PSO-P) for Industrial Reinforcement Learning Problems||A. Fischbach|
|S-2||17.07.2017||Rehbach||Electrostatic Precipitator Optimization using a model-based Evolutionary Algorithm||T. Bartz-Beielstein|
|S-3||17.07.2017||Mehnen, Goodman, Bartz-Beielstein||Ask the experts / Getting a job||E. Goodman|
Dedicated to the discussion of issues related to the practical application of EC. Organized in cooperation with Synergy Project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 692286.