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Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, real-world problems are not always readily available for benchmarking. For example, evaluation costs may be too high, or resources are unavailable (e.g., software or equipment). As a solution, data from previous evaluations can be used to train surrogate models which are then used for benchmarking. The goal is to generate test functions on which the performance of an algorithm is similar to that on the real-world objective function. However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived. This is especially problematic when the training data becomes sparse. The resulting benchmarks may not reflect the landscape features of the ground-truth, are too easy, and may lead to biased conclusions.
To resolve this, we use simulation of Gaussian processes instead of estimation (or prediction). This retains the covariance properties estimated during model training. While previous research suggested a decomposition-based approach for a small-scale, discrete problem, we show that the spectral simulation method enables simulation for continuous optimization problems. In a set of experiments with an artificial ground-truth, we demonstrate that this yields more accurate benchmarks than simply predicting with the Gaussian process model.
Multi-criteria optimization has gained increasing attention during the last decades. This article exemplifies multi-criteria features, which are implemented in the statistical software package SPOT. It describes related software packages such as mco and emoa and gives a comprehensive introduction to simple multi criteria optimization tasks. Several hands-on examples are used for illustration. The article is well-suited as a starting point for performing multi-criteria optimization tasks with SPOT.
An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.
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.
Formerly, multi-criteria optimization algorithms were often tested using tens of thousands function evaluations. In many real-world settings function evaluations are very costly or the available budget is very limited. Several methods were developed to solve these cost-extensive multi-criteria optimization problems by reducing the number of function evaluations by means of surrogate optimization. In this study, we apply different multi-criteria surrogate optimization methods to improve (tune) an event-detection software for water-quality monitoring. For tuning two important parameters of this software, four state-of-the-art methods are compared: S-Metric-Selection Efficient Global Optimization (SMS-EGO), S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO, Euclidean Distance based Expected Improvement Euclid-EI (here referred to as MEI-SPOT due to its implementation in the Sequential Parameter Optimization Toolbox SPOT) and a multi-criteria approach based on SPO (MSPOT). Analyzing the performance of the different methods provides insight into the working-mechanisms of cutting-edge multi-criteria solvers. As one of the approaches, namely MSPOT, does not consider the prediction variance of the surrogate model, it is of interest whether this can lead to premature convergence on the practical tuning problem. Furthermore, all four approaches will be compared to a simple SMS-EMOA to validate that the use of surrogate models is justified on this problem.
The fashion industry is one of the most harmful industries in the world. Many garments are produced and dyed on a petroleum base; vast amounts of water are used in the production of textiles and garments; and environmentally harmful chemicals are released into the environment during production. Working towards sustainability in fashion is more relevant than ever. One way of doing this is to design garments with nature. To do so, I am presenting an example of biotechnology in the field of dyeing techniques. The project is innovative and future-driven in that it offers alternatives to industrial, petroleum-based dyeing techniques. A number of bacteria produce different colored pigments. The bacteria strain Janthinobacterium lividum, for example, is considered nontoxic and safe to handle. It is found in nature on the skin of the red-backed salamander. The bacteria produce dark purple pigments called violacein. With the pigment’s antifungal and antibacterial characteristics, it protects itself and its host from intruders. Can the pigment, however, be applied to dye textiles?
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.
Surrogate-based optimization and nature-inspired metaheuristics have become the state of the art in solving real-world optimization problems. Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization. Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field.
This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding. Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research. However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies. The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes. In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.
Epidemic Geographies
(2023)
This essay is a shortened version of a final BA thesis, written during the global pandemic of covid-19, dominating media reports, public life, and private experience in the quarantine society in the months of April to July 2020. Yet this thesis was also a test, a quiet personal one. To focus on current conditions and events allowed to shift the perspective from familiar contexts to unknown environments. It allowed to try out whether the subjects of our studies can be applied to a ‘real world context’ besides works that often only retrospectively comment on preexisting conditions. Parallel to this text two video works were developed. While the first (Heatmap Urbanism - https://vimeo.com/469567011) offers a visual inquiry into the urban implications of pandemic contact tracing, the second (Relational Topographies - https://vimeo.com/469582311) presents the cartographic speculations that are conceived in this essay.