Modern embedded systems comprise more and more software. This yields novel challenges in development and quality assurance. Complex software interactions may lead to serious performance issues that can have a crucial economic impact if they are not resolved during development. Henceforth, we decided to develop and evaluate a machine learning-based approach to identify performance issues. Our experiments using real-world data show the applicability of our methodology and outline the value of an integration into modern software processes such as continuous integration.

Electrical motors need specific parametrizations to run in highly specialized use cases. However, finding such parametrizations may need a lot of time and expert knowledge. Furthermore, the task gets more complex as multiple optimization goals interplay. Thus, we propose a novel approach using Bayesian Optimization to find optimal configuration parameters for an electric motor. In addition, a multi-objective problem is present as two different and competing objectives must be optimized. At first, the motor must reach a desired revolution per minute as fast as possible. Afterwards, it must be able to continue running without fluctuating currents. For this task, we utilize Bayesian Optimization to optimize parameters. In addition, the evolutionary algorithm NSGA-II is used for the multi-objective setting, as NSGA-II is able to find an optimal pareto front. Our approach is evaluated using three different motors mounted to a test bench. Depending on the motor, we are able to find good pa rameters in about 60-100%.

Touch interfaces are human machine interface (HMI) that can be found in a wide range of products ranging from mobile phones over cars to home appliances.Many of these HMIs measure digital signals which are used to detect touch events.These signals are processed using filters in order to decide whether there is a touch event or not.The filterchain must be functional even if the signal contains heavy noise.Thus a precise calibration of the individual filters is necessary.We employ a genetic algorithm (GA) to choose the filter parameters automatically.We evaluate our approach in a series of experiments which includes simulated as well as real data.We additionally compare our GA with manually calibrated parameters and thereby show the superiority of our method in terms of the accuracy of the calibration provided.A cost-intensive manual calibration can thus be avoided.

Human machine interfaces (HMI) have become a part of our daily lives.They are an essential part of a variety of products ranging from computers over smart phones to home appliances.Customer's requirements for HMIs are rising and so does the complexity of the devices.Several years ago, many products had a rather simple HMI such as mere buttons.Nowadays lots of devices have screens that display complex text messages and a variety of objects such as icons.This leads to new challenges in testing, the goal of which it is to ensure quality and to find errors.We combine a genetic algorithm with computer vision techniques in order to solve two testing use cases located in the automated verification of displays.Our method has a low runtime and can be used on low budget equipment such as Raspberry Pi which reduces the operational cost in practice.

XCS constitutes the most deeply investigated classifier system today. It offers strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks, among which the Deep-Q-Network (DQN) being a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCS barely takes advantage of similar mechanisms that leverage remembered raw experiences. To bridge this gap, this paper investigates the benefits of extending XCS with ER. We demonstrate that for single-step tasks ER yields strong improvements in terms of sample efficiency. On the downside, however, we reveal that ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains.

Testing is a crucial part in the development of a new product.Due to the change from manual testing to automated testing, companies can rely on a higher number of tests.There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary.This prioritization problem has just gotten into the focus of reinforcement learning.A neural network and an XCS classifier system have been applied to this task.Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs.In this work we show that XCSF is superior to both the neural network and XCS for this problem.

The minimum set cover problem (MSCP) is one of the first NP-hard optimization problems discovered.Theoretically it has a bad worst case approximation ratio.As the MSCP turns out to appear in several real world problems, various approaches exist where evolutionary algorithms and metaheuristics are utilized in order to achieve good average case results.This work is intended to revisit and compare current results regarding the application of metaheuristics for the MSCP.Therefore, a recapitulation of the MSCP and its classification into the class of NP-hard optimization problems are provided first.After an overview of notable approximation methods, the focus is shifted towards a brief review of existing metaheuristics which were adapted for the MSCP.In order to allow for a targeted comparison of the existing algorithms, the theoretical worst case complexities in terms of the big O-notation are derived first.This is followed by an empirical study where the identified metaheuristics are examined.Here we use Steiner triple systems, Beasley's OR library, and introduce a new class of instances.Several of the considered approaches achieve close to optimal results.However, our analysis reveals significant differences in terms of runtime and shows that some approaches may even have exponential runtime.