Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen
OPUS4-47 unpublished Zaefferer, Martin; Gaida, Daniel; Bartz-Beielstein, Thomas Multi-fidelity Modeling and Optimization of Biogas Plants An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension. 2014 urn:nbn:de:hbz:832-cos-644 Fakultät 10 / Institut für Informatik
OPUS4-477 Arbeitspapier Zaefferer, Martin; Fischbach, Andreas; Naujoks, Boris; Bartz-Beielstein, Thomas Simulation-based Test Functions for Optimization Algorithms When designing or developing optimization algorithms, test functions are crucial to evaluate performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world applications. Previously, test functions with real-world relevance were generated by training a machine learning model based on real-world data. The model estimation is used as a test function. We propose a more principled approach using simulation instead of estimation. Thus, relevant and varied test functions are created which represent the behavior of real-world fitness landscapes. Importantly, estimation can lead to excessively smooth test functions while simulation may avoid this pitfall. Moreover, the simulation can be conditioned by the data, so that the simulation reproduces the training data but features diverse behavior in unobserved regions of the search space. The proposed test function generator is illustrated with an intuitive, one-dimensional example. To demonstrate the utility of this approach it is applied to a protein sequence optimization problem. This application demonstrates the advantages as well as practical limits of simulation-based test functions. 2017 12 urn:nbn:de:hbz:832-cos4-4777 Fakultät für Informatik und Ingenieurwissenschaften (F10)
OPUS4-753 unpublished Stork, Jörg; Eiben, A.E.; Bartz-Beielstein, Thomas A new Taxonomy of Continuous Global Optimization Algorithms Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field. This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms. 2018 52 urn:nbn:de:hbz:832-cos4-7538 Fakultät für Informatik und Ingenieurwissenschaften (F10)
OPUS4-696 Bericht Stork, Jörg; Bartz-Beielstein, Thomas Global Optimization Strategies: Analogies to Human Behavior This short article presents a new taxonomy for modern global optimization heuristics based on analogies to human behavior. 2018 3 urn:nbn:de:hbz:832-cos4-6967
OPUS4-789 Arbeitspapier Rehbach, Frederik; Zaefferer, Martin; Stork, Jörg; Bartz-Beielstein, Thomas Comparison of Parallel Surrogate-Assisted Optimization Approaches The availability of several CPU cores on current computers enables parallelization and increases the computational power significantly. Optimization algorithms have to be adapted to exploit these highly parallelized systems and evaluate multiple candidate solutions in each iteration. This issue is especially challenging for expensive optimization problems, where surrogate models are employed to reduce the load of objective function evaluations. This paper compares different approaches for surrogate modelbased optimization in parallel environments. Additionally, an easy to use method, which was developed for an industrial project, is proposed. All described algorithms are tested with a variety of standard benchmark functions. Furthermore, they are applied to a real-world engineering problem, the electrostatic precipitator problem. Expensive computational fluid dynamics simulations are required to estimate the performance of the precipitator. The task is to optimize a gas-distribution system so that a desired velocity distribution is achieved for the gas flow throughout the precipitator. The vast amount of possible configurations leads to a complex discrete valued optimization problem. The experiments indicate that a hybrid approach works best, which proposes candidate solutions based on different surrogate model-based infill criteria and evolutionary operators. 2018 12 urn:nbn:de:hbz:832-cos4-7899 Fakultät für Informatik und Ingenieurwissenschaften (F10)
OPUS4-32 Bericht Flasch, Oliver A Friendly Introduction to RGP RGP is genetic programming system based on, as well as fully integrated into, the R environment. The system implements classical tree-based genetic programming as well as other variants including, for example, strongly typed genetic programming and Pareto genetic programming. It strives for high modularity through a consistent architecture that allows the customization and replacement of every algorithm component, while maintaining accessibility for new users by adhering to the "convention over configuration" principle. 2013 urn:nbn:de:hbz:832-cos-446 Fakultät 10 / Institut für Informatik
OPUS4-432 Arbeitspapier Fischbach, Andreas; Zaefferer, Martin; Stork, Jörg; Friese, Martina; Bartz-Beielstein, Thomas From Real World Data to Test Functions When researchers and practitioners in the field of computational intelligence are confronted with real-world problems, the question arises which method is the best to apply. Nowadays, there are several, well established test suites and well known artificial benchmark functions available. However, relevance and applicability of these methods to real-world problems remains an open question in many situations. Furthermore, the generalizability of these methods cannot be taken for granted. This paper describes a data-driven approach for the generation of test instances, which is based on real-world data. The test instance generation uses data-preprocessing, feature extraction, modeling, and parameterization. We apply this methodology on a classical design of experiment real-world project and generate test instances for benchmarking, e.g. design methods, surrogate techniques, and optimization algorithms. While most available results of methods applied on real-world problems lack availability of the data for comparison, our future goal is to create a toolbox covering multiple data sets of real-world projects to provide a test function generator to the research community. 2016 24 urn:nbn:de:hbz:832-cos4-4326 Fakultät für Informatik und Ingenieurwissenschaften (F10)
OPUS4-592 Bericht Bartz-Beielstein, Thomas; Gentile, Lorenzo; Zaefferer, Martin In a Nutshell: Sequential Parameter Optimization The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the Sequential Parameter Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking. 2017 46 urn:nbn:de:hbz:832-cos4-5928 Fakultät für Informatik und Ingenieurwissenschaften (F10)
OPUS4-18 Bericht Bartz-Beielstein, Thomas Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments. 2012 2194-2870 urn:nbn:de:hbz:832-cos-279 Fakultät 10 / Institut für Informatik