Deterministic Wireless Sensor Networks over IEEE 802.15.4 can provide latency-bounded transmission of flows, which is an important enabler for current and future Internet of Things (IoT) use cases. To realise such networks, a viable routing and scheduling solution must be found that can accept all given flows and maintain their latency requirements. The joint routing and scheduling (JRaS) problem promises optimal routing and scheduling decisions-however, at the expense of very high computation times. To overcome this issue, we propose efficient modifications to the separate routing and scheduling problems, such that we can obtain a success rate similar to the optimal JRaS approach. However, our solutions can be found in much less time. We conduct extensive performance evaluations for different problem complexities and found a speedup in the range of 3.03× up to 6.09× compared to JRaS while having almost no statistical difference in success rates.

Real world datasets might contain duplicate or redundant attributes—or even pure noise—which may not be filtered out by data preprocessing algorithms. This might be problematic, as it decreases the performance of learning algorithms. Cartesian Genetic Programming (CGP) is able to choose its own input attributes by design. Thus, we hypothesize that CGP should be able to ignore redundant or noise attributes. In this work, we empirically show that CGP is indeed able to handle such problematic datasets. For this task, six different datasets are extended with different kinds of redundancies: Duplicated-, duplicated and noised-, and pure noise attributes. Different numbers of unwanted attributes are examined, and we present our results which indicate that CGP is robust against additional redundant or noisy attributes in a dataset. We show that there is no decrease in performance as well as no change in CGP’s convergence behaviour.

This work presents and evaluates a novel modification to existing mutation operators for Cartesian Genetic Programming (CGP). We discuss and highlight a so far unresearched limitation of how CGP explores its search space which is caused by certain nodes being inactive for long periods of time. Our new mutation operator is intended to avoid this by associating each node with a dynamically changing weight. When mutating a connection between nodes, those weights are then used to bias the probability distribution in favour of inactive nodes. This way, inactive nodes have a higher probability of becoming active again. We include our mutation operator into two variants of CGP and benchmark both versions on four Boolean learning tasks. We analyse the average numbers of iterations a node is inactive and show that our modification has the intended effect on node activity. The influence of our modification on the number of iterations until a solution is reached is ambiguous if the same number of nodes is used as in the baseline without our modification. However, our results show that our new mutation operator leads to fewer nodes being required for the same performance; this saves CPU time in each iteration.

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%.

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