@techreport{ZaeffererFischbachNaujoksetal.2017,
  type      = {Working Paper},
  author    = {Zaefferer, Martin and Fischbach, Andreas and Naujoks, Boris and Bartz-Beielstein, Thomas},
  title     = {Simulation-based Test Functions for Optimization Algorithms},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-4777},
  pages     = {12},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@techreport{StorkZaeffererFischbachetal.2017,
  type      = {Working Paper},
  author    = {Stork, J{\"o}rg and Zaefferer, Martin and Fischbach, Andreas and Rehbach, Frederik and Bartz-Beielstein, Thomas},
  title     = {Surrogate-Assisted Learning of Neural Networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-5935},
  pages     = {22},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@techreport{RebolledoCKreyBartzBeielsteinetal.2016,
  author    = {Rebolledo C., Margarita A. and Krey, Sebastian and Bartz-Beielstein, Thomas and Flasch, Oliver and Fischbach, Andreas and Stork, J{\"o}rg},
  title     = {Modeling and Optimization of a Robust Gas Sensor},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-3399},
  pages     = {14},
  year      = {2016},
  abstract  = {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.},
  subject      = {Soft Computing},
  language  = {en}
}
@techreport{FischbachZaeffererStorketal.2016,
  type      = {Working Paper},
  author    = {Fischbach, Andreas and Zaefferer, Martin and Stork, J{\"o}rg and Friese, Martina and Bartz-Beielstein, Thomas},
  title     = {From Real World Data to Test Functions},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-4326},
  pages     = {24},
  year      = {2016},
  abstract  = {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.},
  subject      = {Modelierung},
  language  = {en}
}
@techreport{FischbachStrohscheinBunteetal.,
  type      = {Working Paper},
  author    = {Fischbach, Andreas and Strohschein, Jan and Bunte, Andreas and Stork, J{\"o}rg and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas},
  title     = {CAAI - A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-8834},
  pages     = {15},
  abstract  = {This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.},
  subject      = {Industrie 4.0},
  language  = {en}
}
@techreport{ChandrasekaranZaeffererMoritzetal.2016,
  type      = {Working Paper},
  author    = {Chandrasekaran, Sowmya and Zaefferer, Martin and Moritz, Steffen and Stork, J{\"o}rg and Friese, Martina and Fischbach, Andreas and Bartz-Beielstein, Thomas},
  title     = {Data Preprocessing: A New Algorithm for Univariate Imputation Designed Specifically for Industrial Needs},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-4331},
  pages     = {24},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@techreport{BartzBeielsteinDoerrBosseketal.,
  type      = {Working Paper},
  author    = {Bartz-Beielstein, Thomas and Doerr, Carola and Bossek, Jakob and Chandrasekaran, Sowmya and Eftimov, Tome and Fischbach, Andreas and Kerschke, Pascal and Lopez-Ibanez, Manuel and Malan, Katherine M. and Moore, Jason H. and Naujoks, Boris and Orzechowski, Patryk and Volz, Vanessa and Wagner, Markus and Weise, Thomas},
  title     = {Benchmarking in Optimization: Best Practice and Open Issues},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos4-9022},
  pages     = {58},
  abstract  = {This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well- specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.},
  subject      = {Optimierung},
  language  = {en}
}