Hooman Shayani (Autodesk Research)
Hooman Shayani’s talk focuses on the central role of Evolutionary Computation in the future of design and manufacturing, and how academics in the field of Evolutionary Computation can make rapid and impactful contributions to the innovations in these industries.
It starts with an introduction to Autodesk Inc, its vision regarding the Future of Making Things (FOMT), Generative Design, and Dreamcatcher project. Hooman Shayani is a Senior Principal Research Scientist at Autodesk Research. He is part of the Design Research team in the office of the CTO, working on Dreamcatcher project, among other projects. Hooman Shayani will present this talk at GECCO 2017 in the Evolutionary Computation in Practice track on Monday, July 17th.
In the Evolutionary Computation in Practice 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. More information about the ECiP track can be found here: http://www.spotseven.de/gecco/evolutionary-computation-in-practice
Call for Participation – GECCO 2017 Competitions
Please consider taking part in one or several GECCO 2017 competitions, even if you cannot come to the conference. The deadlines are in mid or late June, so there is still time to start working on an entry.
One of the competitions, the GECCO Industrial Challenge “Monitoring of drinking-water quality” is organized by members of the SPOTSeven Lab. More information can be found here: http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2017/ Continue reading
Optimization and Learning Through Evolutionary Computation (K. Deb)
Evolutionary and classical algorithms are most often applied to find one or more high-performing solutions for an industrial problem. Such an effort takes a considerable time to formulate the resulting optimization problem, customize an existing algorithm to make it efficient to solve the problem in a reasonable computational time, and evaluate the obtained solutions for their sensitivities to parameter changes. Kalyanmoy Deb (Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA) will present this talk at GECCO 2017 in the Evolutionary Computation in Practice track on Monday, July 17th.
In the Evolutionary Computation in Practice 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.
Highlights of the GECCO 2017 Industrial Challenge : Continue reading
Sabine Wurth-Goller (Marketing Manager, SE), Prof. Dr. Thomas Bartz-Beielstein, Shinichi Takano (Managing Director, SE), and Thomas Will (Head of Steam Generation, SE)
Shinichi Takano (Managing Director, Steinmüller Engineering) visited the SPOTSeven Lab at the TH Köln Campus in Gummersbach last week. Continue reading
The article “Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data” (Alexis Sardá-Espinosaa, Subanatarajan Subbiah, Thomas Bartz-Beielstein), which will be published in the journal “Engineering Applications of Artificial Intelligence“, can be freely downloaded until May 20, 2017 from
Anyone who clicks on the link until May 20, 2017, will be taken to the final version of your article on ScienceDirect for free. No sign up or registration is needed – just click and read!
Abstract: Computational tools for the analysis of data gathered by monitoring systems are necessary because the amount of data steadily increases. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner.
Keywords: Decision tree; Conditional inference tree; Health condition monitoring; Machine learning; Knowledge extraction
Authors: Alexis Sardá-Espinosa (ABB AG German Research Center, Technische Hochschule Köln), Subanatarajan Subbiah (ABB AG German Research Center), Thomas Bartz-Beielstein (Technische Hochschule Köln)
Here is the DOI: 10.1016/j.engappai.2017.03.008
Das Pionier-Unternehmen der Heimarbeit, IBM, schafft Stück für Stück das Home Office ab. Mehr: http://www.faz.net/-gyl-8w6x1