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Chapter
Case Study II: Tuning of Gradient Boosting (xgboost)
This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Extreme Gradient Boosting (XGBoost) method and its implementation xgboost was chosen, because it is ...
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Chapter
Application
Parts of the following section are based on “Model-based evolutionary algorithm for optimization of gas distribution systems in power plant electrostatic precipitators” by Schagen et al. [1] and “Comparison of Pa...
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Chapter
Introduction
The practical optimization of many applications is accompanied by a significant amount of cost for each conducted experiment. Such costs might stem from the time an employee requires to perform experiments, co...
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Chapter
Final Evaluation
At the beginning of this document, we expressed existing challenges in the field of parallel SBO. To conclude this document we will discuss our contributions to these challenges. For this purpose, we would lik...
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Book
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Chapter
Background
The following chapter briefly presents background knowledge on SBO (Sect. 2.1), infill criteria (Sect. 2.1), and EAs (Sect. 2.2). We refer to more in-depth literature where needed. The chapter continues by presen...
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Chapter
Case Study I: Tuning Random Forest (Ranger)
This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Random Forest (RF) method and its implementation ranger was chosen because it is the method of the f...
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Chapter
Methods/Contributions
The following chapter presents the research contributions of this work. It focuses on advancing techniques for the parallel optimization of expensive-to-evaluate functions. Rigorous methods for analyzing and a...
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Chapter
Case Study III: Tuning of Deep Neural Networks
A surrogate model based Hyperparameter Tuning (HPT) approach for Deep Learning (DL) is presented. This chapter demonstrates how the architecture-level parameters (hyperparameters) of Deep Neural Networks (DNNs...
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Chapter
Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives
The focus of this paper lies on automatic and interactive tuning methods for stochastic optimization algorithms, e.g., evolutionary algorithms. Algorithm tuning is important because it helps to avoid wrong par...
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Chapter and Conference Paper
Parallelized Bayesian Optimization for Expensive Robot Controller Evolution
An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simula...
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Chapter and Conference Paper
Continuous Optimization Benchmarks by Simulation
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 alwa...
