TY - RPRT
A1 - Stork, Jörg
A1 - Bartz-Beielstein, Thomas
T1 - Global Optimization Strategies: Analogies to Human Behavior
N2 - This short article presents a new taxonomy for modern global optimization heuristics based on analogies to human behavior.
T3 - CIplus - 02/2018
KW - Taxonomy
KW - Optimization
KW - Heuristics
KW - Surrogate
KW - Metaheuristik
KW - Taxonomie
KW - Optimierung
Y1 - 2018
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-6967
ER -
TY - INPR
A1 - Stork, Jörg
A1 - Eiben, A.E.
A1 - Bartz-Beielstein, Thomas
T1 - A new Taxonomy of Continuous Global Optimization Algorithms
N2 - 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.
T3 - CIplus - 4/2018
KW - Optimization
KW - Taxonomy
KW - Evolutionary Computation
KW - Metaheuristics
KW - Surrogate
KW - Globale Optimierung
KW - Taxonomie
KW - Evolutionärer Algorithmus
KW - Metaheuristik
Y1 - 2018
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-7538
ER -
TY - RPRT
A1 - Rehbach, Frederik
A1 - Zaefferer, Martin
A1 - Stork, Jörg
A1 - Bartz-Beielstein, Thomas
T1 - Comparison of Parallel Surrogate-Assisted Optimization Approaches
N2 - 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.
T3 - CIplus - 7/2018
KW - Optimization
KW - Surrogates
KW - Modeling
KW - Parallelization
KW - Electrostatic Precipitator
Y1 - 2018
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-7899
ER -
TY - RPRT
A1 - Stork, Jörg
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Rehbach, Frederik
A1 - Bartz-Beielstein, Thomas
T1 - Surrogate-Assisted Learning of Neural Networks
N2 - Surrogate-assisted optimization has proven to be very successful if applied to industrial problems. The use of a data-driven surrogate model of an objective function during an optimization cycle has many bene ts, such as being cheap to evaluate and further providing both information about the objective landscape and the parameter space. In preliminary work, it was researched how surrogate-assisted optimization can help to optimize the structure of a neural network (NN) controller. In this work, we will focus on how surrogates can help to improve the direct learning process of a transparent feed-forward neural network controller. As an initial case study we will consider a manageable real-world control task: the elevator supervisory group problem (ESGC) using a simplified simulation model. We use this model as a benchmark which should indicate the applicability and performance of surrogate-assisted optimization to this kind of tasks. While the optimization process itself is in this case not onsidered expensive, the results show that surrogate-assisted optimization is capable of outperforming metaheuristic optimization methods for a low number of evaluations. Further the surrogate can be used for signi cance analysis of the inputs and weighted connections to further exploit problem information.
T3 - CIplus - 8/2017
Y1 - 2017
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-5935
ER -
TY - RPRT
A1 - Fischbach, Andreas
A1 - Zaefferer, Martin
A1 - Stork, Jörg
A1 - Friese, Martina
A1 - Bartz-Beielstein, Thomas
T1 - From Real World Data to Test Functions
N2 - 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.
T3 - CIplus - 6/2016
KW - Modeling
KW - Optimization
KW - Benchmarking
KW - Test Function
KW - Modelierung
KW - Optimierung
KW - Benchmarking
KW - Funktionstest
Y1 - 2016
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4326
ER -
TY - RPRT
A1 - Chandrasekaran, Sowmya
A1 - Zaefferer, Martin
A1 - Moritz, Steffen
A1 - Stork, Jörg
A1 - Friese, Martina
A1 - Fischbach, Andreas
A1 - Bartz-Beielstein, Thomas
T1 - Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs
N2 - Data pre-processing is a key research topic in data mining because it plays a
crucial role in improving the accuracy of any data mining algorithm. In most
real world cases, a significant amount of the recorded data is found missing
due to most diverse errors. This loss of data is nearly always unavoidable.
Recovery of missing data plays a vital role in avoiding inaccurate data
mining decisions. Most multivariate imputation methods are not compatible
to univariate datasets and the traditional univariate imputation techniques
become highly biased as the missing data gap increases. With the current
technological advancements abundant data is being captured every second.
Hence, we intend to develop a new algorithm that enables maximum
utilization of the available big datasets for imputation. In this paper, we
present a Seasonal and Trend decomposition using Loess (STL) based
Seasonal Moving Window Algorithm, which is capable of handling patterns
with trend as well as cyclic characteristics. We show that the algorithm is
highly suitable for pre-processing of large datasets.
T3 - CIplus - 7/2016
KW - Time Series
KW - Imputation
KW - Univariate Data
Y1 - 2016
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4331
ER -
TY - RPRT
A1 - Rebolledo C., Margarita A.
A1 - Krey, Sebastian
A1 - Bartz-Beielstein, Thomas
A1 - Flasch, Oliver
A1 - Fischbach, Andreas
A1 - Stork, Jörg
T1 - Modeling and Optimization of a Robust Gas Sensor
N2 - In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model.
The models are build on industrial data for the development of a robust gas sensor.
The data contain limited amount of samples and a high variance.
The mean square error of the models implemented in a test dataset is used as the comparison strategy.
The results indicate that standard linear regression approaches as well as Kriging and GP show good results,
whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.
T3 - CIplus - 3/2016
KW - Soft Computing
KW - Lineare Regression
KW - Sensortechnik
KW - Bayesian Learning
KW - Regression
Y1 - 2016
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-3399
ER -
TY - RPRT
A1 - Breiderhoff, Beate
A1 - Bartz-Beielstein, Thomas
A1 - Naujoks, Boris
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Flasch, Oliver
A1 - Friese, Martina
A1 - Mersmann, Olaf
A1 - Stork, Jörg
T1 - Simulation and Optimization of Cyclone Dust Separators
N2 - Cyclone Dust Separators are devices often used to filter solid particles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a minimal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise variables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective optimization algorithm. For the harder, stochastic optimization problem, a surrogate-model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget.
T3 - CIplus - 4/2013
KW - Soft Computing
KW - Evolutionärer Algorithmus
KW - Mehrkriterielle Optimierung
KW - Entstauber
KW - Simulation
KW - Mehrkriterielle Optimierung
KW - Surrogat-Modellierung
KW - Sequentielle Parameter Optimierung
KW - Zylon Enstauber
KW - Multiobjective Optimization
KW - Multi-Criteria Optimization
KW - Surrogate Modeling
KW - Sequential Parameter Optimization
KW - Cyclone Dust Separator
Y1 - 2013
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-470
ER -
TY - RPRT
A1 - Friese, Martina
A1 - Stork, Jörg
A1 - Ramos Guerra, Ricardo
A1 - Bartz-Beielstein, Thomas
A1 - Thaker, Soham
A1 - Flasch, Oliver
A1 - Zaefferer, Martin
T1 - UniFIeD Univariate Frequency-based Imputation for Time Series Data
N2 - This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are independent from the underlying error measurements.
T3 - CIplus - 5/2013
KW - Zeitreihe
KW - Prognose
KW - Datenanalyse
KW - Vorverarbeitung
KW - Zeitreihenanalyse
KW - Fehlende Daten
KW - Time-series
KW - Missing Data
KW - Imputation
Y1 - 2013
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-493
SN - 2194-2870
ER -
TY - CHAP
A1 - Heinerman, Jacqueline
A1 - Stork, Jörg
A1 - Rebolledo Coy, Margarita Alejandra
A1 - Hubert, Julien
A1 - Eiben, A.E.
A1 - Bartz-Beielstein, Thomas
A1 - Haasdijk, Evert
T1 - Is Social Learning More Than Parameter Tuning?
N2 - Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts. No explanation has been advanced for that observation. In this research, we present experimental results suggesting that a lack of tuning of the parameters in social learning experiments could be the cause. In other words: the better the parameter settings are tuned, the less social learning can improve the system performance.
T3 - CIplus - 5/2017
KW - Evolutionary Robotics
KW - Social Learning
KW - Neural Networks
KW - Parameter Tuning
Y1 - 2017
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-5451
ER -