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In this paper, we evaluate the application of Bayesian Optimization (BO) to discrete event simulation (DES) models. In a first step, we create a simple model, for which we know the optimal set of parameter values in advance. We implement the model in SimPy, a framework for DES written in Python. We then interpret the simulation model as a black box function subject to optimization. We show that it is possible to find the optimal set of parameter values using the open source library GPyOpt. To enhance our evaluation, we create a second and more complex model. To better handle the complexity of the model, and to add a visual component, we build the second model in Simio, a commercial off-the-shelf simulation modeling tool. To apply BO to a model in Simio, we use the Simio API to write an extension for optimization plug-ins. This extension encapsulates the logic of the BO algorithm, which we deployed as a web service in the cloud.
The fact that simulation models are black box functions with regard to their behavior and the influence of their input parameters makes them an apparent candidate for Bayesian Optimization (BO). Simulation models are multivariable and stochastic, and their behavior is to a large extent unpredictable. In particular, we do not know for sure which input parameters to adjust to maximize (or minimize) the model’s outcome. In addition, the complex models can take a substantial amount of time to run.
Bayesian Optimization is a sequential and self-learning algorithm to optimize black box functions similar to as we find them in simulation models: they contain a set of parameters for which we want to identify the optimal set, they are expensive to evaluate, and they exhibit stochastic noise. BO has proven to efficiently optimize black box functions from varius disciplines. Among those, and most notably, it is successfully applied in machine learning algorithms to optimize hyperparameters.
Studies on nutrition have historically concentrated on food-shortages and over-nutrition. The physiological states of feeling hungry or being satiated and its dynamics in food choices, dietary patterns, and nutritional behavior, have not been the focus of many studies. Currently, visual analytic using easy-to-use tooling offers applicability in a wide-range of disciplines. In this interdisciplinary pilot-study we tested a novel visual analytic software to assess dietary patterns and food choices for greater understanding of nutritional behavior when hungry and when satiated. We developed software toolchain and tested the hypotheses that there is no difference between visual search patterns of dishes 1) when hungry and when satiated and 2) in being vegetarian and non-vegetarian. Results indicate that food choices can be deviant from dietary patterns but correlate slightly with dish-gazing. Further, scene perception probably could vary between being hungry and satiated. Understanding t he complicated relationship between scene perception and nutritional behavioral patterns and scaling up this pilot-study to a full-study using our introduced software approaches is indispensable.