Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints

Controller tuning based on black-box optimization allows to automatically tune performance-critical parameters w.r.t. mostly arbitrary high-level closed-loop control objectives. However, a comprehensive benchmark of different black-box optimizers for control engineering problems has not yet been conducted. Therefore, in this contribution, 11 different versions of Bayesian optimization (BO) are compared with seven metaheuristics and other baselines on a set of ten deterministic simulative single-objective tuning problems in control. Results indicate that deterministic noise, low multimodality, and substantial areas with infeasible parametrizations (crash constraints) characterize control engineering tuning problems. Therefore, a flexible method to handle crash constraints with BO is presented. A resulting increase in sample efficiency is shown in comparison to standard BO. Furthermore, benchmark results indicate that pattern search (PS) performs best on a budget of 25 d objective function evaluations and a problem dimensionality d of d = 2. Bayesian adaptive direct search, a combination of BO and PS, is shown to be most sample efficient for 3 <= d <= 5. Using these optimizers instead of random search increases controller performance by on average 6.6% and up to 16.1%.Comment: 13 pages, 9 figure

Buying spatially-coordinated ecosystem services : an experiment on the role of auction format and communication

The study was carried out as a part of the NEWFOREX project (New Ways to Value and Market Forest Externalities, FP7-KBBE-2009-3, Project no. 243950). It was also supported by the French National Research Agency (ANR) as part of the "Investissements d’Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).Procurement auctions are one of several policy tools available to incentivise the provision of ecosystem services and biodiversity conservation. Successful biodiversity conservation often requires a landscape-scale approach and the spatial coordination of participation, for example in the creation of wildlife corridors. In this paper, we use a laboratory experiment to explore two features of procurement auctions in a forest landscape: the pricing mechanism (uniform vs. discriminatory) and availability of communication (chat) between potential sellers. We modify the experimental design developed by Reeson et al. (2011) by introducing uncertainty (and hence heterogeneity) in the production value of forest sites as well as an automated, endogenous stopping rule. We find that discriminatory pricing yields to greater environmental benefits per government dollar spent, chiefly because it is easier to construct long corridors. Chat also facilitates such coordination but also seems to encourage collusion in sustaining high prices for the most environmentally attractive plots. These two effects offset each other, making chat neutral from the viewpoint of maximizing environmental effect per dollar spent.PostprintPeer reviewe

Reconsidering the value of covert research: the role of ambiguous consent in participant observation

In this article, we provide a nuanced perspective on the benefits and costs of covert research. In particular, we illustrate the value of such an approach by focusing on covert participant observation. We posit that all observational studies sit along a continuum of consent, with few research projects being either fully overt or fully covert due to practical constraints and the ambiguous nature of consent itself. With reference to illustrative examples, we demonstrate that the study of deviant behaviors, secretive organizations and socially important topics is often only possible through substantially covert participant observation. To support further consideration of this method, we discuss different ethical perspectives and explore techniques to address the practical challenges of covert participant observation, including; gaining access, collecting data surreptitiously, reducing harm to participants, leaving the site of study and addressing ethical issues

A model of base-call resolution on broad-spectrum pathogen detection resequencing DNA microarrays

Oligonucleotide microarrays offer the potential to efficiently test for multiple organisms, an excellent feature for surveillance applications. Among these, resequencing microarrays are of particular interest, as they possess additional unique capabilities to track pathogens’ genetic variations and perform detailed discrimination of closely related organisms. However, this potential can only be realized if the costs of developing the detection microarray are kept at a manageable level. Selection and verification of the probes are key factors affecting microarray design costs that can be reduced through the development and use of in silico modeling. Models created for other types of microarrays do not meet all the required criteria for this type of microarray. We describe here in silico methods for designing resequencing microarrays targeted for multiple organism detection. The model development presented here has focused on accurate base-call prediction in regions that are applicable to resequencing microarrays designed for multiple organism detection, a variation from other uses of a predictive model in which perfect prediction of all hybridization events is necessary. The model will assist in simplifying the design of resequencing microarrays and in reduction of the time and costs required for their development for new applications