Category Archives: Computational Intelligence

Special Session “Multiobjective Optimization with Surrogate Models” (Pre-announcement)

Pre-Announcement: 2016 IEEE WCCI/CEC Special Session 

The following proposal was provisionally accepted by the IEEE WCCI/CEC organizers:

Title: Multiobjective Optimization with Surrogate Models


Aim of this Special Session Continue reading

Springer Handbook of Computational Intelligence

They finally arrived this week: Two editor/author copies of the “Springer Handbook of Computational Intelligence“.
The handbook is described by Springer as follows:
“The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence…Content is organized in seven parts:  foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
Together with Joern Mehnen (Cranfield University) Prof. Bartz-Beielstein served as Part Editor for “Real-World Applications”.


Free paper: What Works Best When? A Framework for Systematic Heuristic Evaluation

Ian Dunning, Swapi Gupta, and John Silverholz (Operations Research Center, Massachusetts Institute of Technology) present a systematic review of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) heuristics papers. They found only 4% publish source code, only 10% compare heuristics with identical hardware and termination criteria, and most experiments are performed with an artificial, homogeneous set of problem instances.
They state:
Why do we see these limitations in empirical testing? Though best practices for empirical testing have long been published in both the industrial engineering/OR literature (Barr et al. 1995, Rardin and Uzsoy 2001, Silberholz and Golden 2010) and the computer science literature (Johnson 2002, Eiben and Jelasity 2002, Bartz-Beielstein 2006, McGeoch 2012), technical barriers exist to carrying out these best practices.Continue reading

Patryk Filipiak Talking About “Proactive Evolutionary Algorithms” in SPOTSeven’s Doctoral Seminar

P. FilipiakPatryk Filipiak, a junior lecturer at the Institute of Computer Science, University of Wroclaw (Poland), visited SPOTSeven lab in March. He is finalizing his PhD thesis concerning the behavior of “Proactive Evolutionary Algorithms in the Dynamic Constrained Optimization Problems” (DCOPs).
He proposed anticipation strategies that can speed up a convergence of candidate solutions in a class of DCOPs and applied these strategies to inverse kinematics problems. Patrick gave an interesting talk in SPOTSeven’s doctoral seminar. The beginning of his talk is shown in the following short video:

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Energy Frankfurt GRID Report: Make Frankfurt am Main carbon free by 2050

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A proposal to make Frankfurt am Main (Germany) carbon free by 2050 was written by Francisca Molina Moreno, Ivan Leiva Lopez, Gabor Szabo, and Davide Dapelo.

Their idea “is based on a virtual division of the city by areas attending to their production and consumption of energy and also their best option to include one or more energy harvesting system such a PV, Geothermal, Vertical Wind.” To handle timetable demand (dynamic demand)  and production, prediction methods developed in the SPOTSeven Lab [1] are proposed. They write:
“It is therefore known that meteorological information is now available such as quantity of solar radiation, wind and speed direction and also power demand. Statistical methods (stochastic), will provide data source of weather prognosis’ algorithms that will estimate the meteorological parameters [1]. By using technology that can automatically monitor and control the systems in a building by checking the local weather and adjusting climate control for example, energy costs can be reduced by as much as 15%…”

Time series plots of the test data range predictions generated by a method studied in [1].

The technical report for the energy transition in Frankfurt (“Make Frankfurt am Main carbon free by 2050”) is available for download here.


[1] O. Flasch, M. Friese, K. Vladislavleva, T. Bartz-Beielstein, O. Mersmann, B. Naujoks, J. Stork, and M. Zaefferer. Comparing ensemble-based forecasting methods for smart-metering data. In A. Esparcia- Alc ́azar, editor, Applications of Evolutionary Computation, volume 7835 of Lecture Notes in Computer Science, pages 172–181. Springer Berlin Heidelberg, 2013.  A preprint is available for download from the SPOTSeven page.

[2] Francisca Molina,Ivan Leiva Lopez, Gabor Szabo, and Davide Dapelo. A proposal to make Frankfurt am Main carbon free by 2050. Technical report for the energy transition in Frankfurt. DOI: 10.13140/2.1.1052.2403 Affiliation: Provadis School of International Management and Technology Frankfurt/Main, Germany

Book on Optimization with R(GP)

Paulo Cortez’s new book “Modern Optimization with R”, published by Springer Verlag, contains practical examples on the successful application of RGP, including examples on time series forecasting. RGP models compare favorably to tuned ARIMA models. See for details. There is also an RGP related Webpage:

Free Overview Article: Evolutionary Algorithms

A pre-peer reviewed version of the following article: Bartz-Beielstein, T. and Branke, J. and Mehnen, J. and Mersmann, O.: Evolutionary Algorithms. WIREs Data Mining Knowl Discov 2014, 4:178- 195. doi:10.1002/widm.1124 is available for download at Cologne Open Science:
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