Category Archives: Publications

Free Preprint: Model-based Methods for Continuous and Discrete Global Optimization

The paper “Model-based Methods for Continuous and Discrete Global Optimization” (T. Bartz-Beielstein, M. Zaefferer) is available for download via Cologne Open Science: https://cos.bibl.th-koeln.de/frontdoor/index/index/docId/435.
Surrogate models gained importance for discrete optimization problems. This paper presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools, and provides details for the case of discrete optimization problems. Six strategies for dealing with discrete data structures are introduced.
A new approach for combining surrogate information via stacking is proposed. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in both application domains. Continue reading

New Paper: A Surrogate Model Assisted EA for Computationally Expensive Design Optimization Problems with Discrete Variables

The paper “A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables” focuses on discrete numerical variables, which are sometimes unavoidable in product design and manufacturing. It was written by Bo Liu (Department of Computing, Glyndwr University, UK), Nan Sun (Department of Engineering, Glyndwr University, UK),  Qingfu Zhang (Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR), Vic Grout (Department of Computing, Glyndwr University, UK), and Georges Gielen (ESAT-MICAS, Katholieke Universiteit Leuven, Belgium). The paper can be  accessed here: http://ieeexplore.ieee.org/abstract/document/7743986/

Related papers, e.g., M. Zaefferer, J. Stork, and T. Bartz-Beielstein, “Distance Measures for Permutations in Combinatorial Efficient Global Optimization” can be found on the SPOTSeven publication page http://www.spotseven.de/publications/.

GPU-based parallel evolution strategy in bioinformatics

The paper “GPU-based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites” (Matthias Leinweber, Thomas Fober, and Bernd Freisleben) presents “a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning.”
The authors applied the SPOT (sequential parameter optimization toolbox) to tune the evolution strategy: “The ES parameters population size, lifetime of individuals, recombination parameter, and mutation rate depend on the problem to be solved, and we used another optimizer, the Sequential Parameter Optimization Toolbox (SPOT) [53], to optimize them.”

The paper can be downloaded from http://doi.ieeecomputersociety.org/10.1109/TCBB.2016.2625793

References Continue reading

26. Workshop Computational Intelligence: Proceedings vom Vorjahr zum freien Download erhältlich

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Das SPOTSeven Lab ist mit zwei Beiträgen bei dem diesjährigen CI Workshop, 24.-25. November 2016 in Dortmund, vertreten:

  • A. Fischbach, J. Stork, M. Zaefferer, S. Krey, T. Bartz-Beielstein, Analyzing Capabilities of Latin Hypercube Designs Compared to Classical Experimental Design Methods
  • Sowmya Chandrasekaran, Steffen Moritz, Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein, Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs

Die Proceedings zum 25. Workshop Computational Intelligence sind beim Verlag Scientific Publishing als Buch oder als PDF zum freien Download  erhältlich.

Book Performance Report: Experimental Methods for the Analysis of Optimization Algorithms

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Since its online publication on October 26, 2010, there has been a total of 15,581 chapter downloads of the book Experimental Methods for the Analysis of Optimization Algorithms. Here are the detailed download figures:

  • 2015: 3,487
  • 2014: 5,924
  • 2013: 3,390
  • 2012: 974
  • 2011: 1,344
  • 2010: 462

 

From the reviews:
“This book belongs on the shelf of anyone interested in carrying out experimental research on algorithms and heuristics for optimization problems. … Don’t keep this book on the shelf: read it, and apply the techniques and tools contained herein to your own algorithmic research project. Your experiments will become more efficient and more trustworthy, and your experimental data will lead to clearer and deeper insights about performance.” (Catherine C. McGeoch, Amherst College)

“Here you will find aspects that are treated scientifically by the experts in this exciting domain offering their up-to-date know-how and even leading into philosophical domains.” (Hans-Paul Schwefel, Technische Universität Dortmund)

“[This] book … is a solid and comprehensive step forward in the right direction. [It] not
only covers adequate comparison of methodologies but also the tools aimed at helping in algorithm design and understanding, something that is being recently referred to as ‘Algorithm Engineering’. [It] is of interest to two distinct audiences. First and foremost, it is targeted at
the whole operations research and management science, artificial intelligence and computer science communities with a loud and clear cry for attention. Strong, sound and reliable tools should be employed for the comparison and assessment of algorithms and also for more structured algorithm engineering. Given the level of detail of some other chapters however, a second potential audience could be made up of those researchers interested in the core topic
of algorithm assessment. The long list of contributors to this book includes top notch and experienced researchers that, together, set the trend in the field. As a result, those interested in this specific area of analysis of optimization algorithms should not miss this book under any circumstance. … The careful, sound, detailed and comprehensive assessment of optimization algorithms is a necessity that requires attention and care. As a result, my opinion is that this book should be followed and that it should be at the top of every experimenter’s table.” (Rubén Ruiz, European Journal of Operational Research, 2011, 214(2):453-456)

Red Elevator Book’s 10th Anniversary

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The “red elevator book” was published ten years ago. It contains results from a cooperations between Sandor Markon, Hajime Kita, Hiroshi Kise, and Thomas Bartz-Beielstein. A review, written by Marja-Liisa Siikonen, can be found here: http://onlinelibrary.wiley.com/doi/10.1002/rnc.1515/full
The red elevator book is published by Springer: http://www.springer.com/de/book/9781846284489