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• Chapter 1: Introduction
• Chapter 2: Tuning
• Chapter 3: Models
• Hyperparameter Tuning Approaches
• Chapter 5: Result Aggregation
• Chapter 6: Relevance of Tuning in Industrial Applications
• Chapter 7: Hyperparameter Tuning in German Official Statistics
• Chapter 8: Case Study I
• Chapter 9: Case Study II
• Chapter 10: Case Study III
• Chapter IV: Case Study IV
• Chapter 12: Global Study.

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

• Infill Criteria for Multiobjective Bayesian Optimization.- Many-Objective Optimization with Limited Computing Budget.- Multi-Objective Bayesian Optimization for Engineering Simulation.- Automatic Configuration of Multi-Objective Optimizers and Multi-Objective Configuration.- Optimization and Visualization in Many-Objective Space Trajectory Design.- Simulation Optimization through Regression or Kriging Metamodels.- Towards Better Integration of Surrogate Models and Optimizers.- Surrogate-Assisted Evolutionary Optimization of Large Problems.- Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems.- Open Issues in Surrogate-Assisted Optimization.- A Parallel Island Model for Hypervolume-Based Many-Objective Optimization.- Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors.
• (source: Nielsen Book Data)

This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That's where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems. .
(source: Nielsen Book Data)

• Adaptation, Self-Adaptation and Parameter Tuning
• Classifier Systems, Differential Evolution and Swarm Intelligence
• Coevolution and Artificial Immune Systems
• Constraint Handling
• Dynamic and Uncertain Environments
• Estimation of Distribution Algorithms and Metamodelling
• Genetic Programming
• Multi-objective Optimisation
• Parallel Algorithms and Hardware Implementations
• Real-World Applications
• Theory.

This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014. The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN XIII also included 9 tutorials. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; classifier system, differential evolution and swarm intelligence; coevolution and artificial immune systems; constraint handling; dynamic and uncertain environments; estimation of distribution algorithms and metamodelling; genetic programming; multi-objective optimisation; parallel algorithms and hardware implementations; real world applications; and theory.

• Overview.- The Future of Experimental Research.- Design and Analysis of Computational Experiments: Overview.- The Generation of Experimental Data for Computational Testing in Optimization.- The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison.- Algorithm Engineering: Concepts and Practice.- Characterizing Algorithm Performance.- Algorithm Survival Analysis.- On Applications of Extreme Value Theory in Optimization.- Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization.- Algorithm Configuration and Tuning.- Mixed Models for the Analysis of Optimization Algorithms.- Tuning an Algorithm Using Design of Experiments.- Using Entropy for Parameter Analysis of Evolutionary Algorithms.- F-Race and Iterated F-Race: An Overview.- The Sequential Parameter Optimization Toolbox.- Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches.
• (source: Nielsen Book Data)

In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.
(source: Nielsen Book Data)

• Evolutionary Local Search for the Super-Peer Selection Problem and the p-Hub Median Problem.- An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem.- Empirical Analysis of Two Different Metaheuristics for Real-World Vehicle Routing Problems.- Guiding ACO by Problem Relaxation: A Case Study on the Symmetric TSP.- Hybrid Local Search Techniques for the Resource-Constrained Project Scheduling Problem.- Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop.- A Hybrid ILS Heuristic to the Referee Assignment Problem with an Embedded MIP Strategy.- On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case.- Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement.- Using Branch & Bound Concepts in Construction-Based Metaheuristics: Exploiting the Dual Problem Knowledge.- Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric.- A Hybrid VNS for Connected Facility Location.- A Memetic Algorithm for the Optimum Communication Spanning Tree Problem.- Hybrid Numerical Optimization for Combinatorial Network Problems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop on Hybrid Metaheuristics, HM 2007, held in Dortmund, Germany. The 14 revised full papers discuss specific aspects of hybridization of metaheuristics, hybrid metaheuristics design, development and testing. With increasing attention to methodological aspects, from both the empirical and theoretical sides, the papers show a representative sample of research in the field of hybrid metaheuristics.
(source: Nielsen Book Data)

• Evolutionary Local Search for the Super-Peer Selection Problem and the p-Hub Median Problem.- An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem.- Empirical Analysis of Two Different Metaheuristics for Real-World Vehicle Routing Problems.- Guiding ACO by Problem Relaxation: A Case Study on the Symmetric TSP.- Hybrid Local Search Techniques for the Resource-Constrained Project Scheduling Problem.- Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop.- A Hybrid ILS Heuristic to the Referee Assignment Problem with an Embedded MIP Strategy.- On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case.- Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement.- Using Branch & Bound Concepts in Construction-Based Metaheuristics: Exploiting the Dual Problem Knowledge.- Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric.- A Hybrid VNS for Connected Facility Location.- A Memetic Algorithm for the Optimum Communication Spanning Tree Problem.- Hybrid Numerical Optimization for Combinatorial Network Problems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop on Hybrid Metaheuristics, HM 2007, held in Dortmund, Germany. The 14 revised full papers discuss specific aspects of hybridization of metaheuristics, hybrid metaheuristics design, development and testing. With increasing attention to methodological aspects, from both the empirical and theoretical sides, the papers show a representative sample of research in the field of hybrid metaheuristics.
(source: Nielsen Book Data)

• Basics.- Research in Evolutionary Computation.- The New Experimentalism.- Statistics for Computer Experiments.- Optimization Problems.- Designs for Computer Experiments.- Search Algorithms.- Results and Perspectives.- Comparison.- Understanding Performance.- Summary and Outlook.
• (source: Nielsen Book Data)

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
(source: Nielsen Book Data)