High-Performance Simulation-Based Optimization
- Responsibility
- Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi, editors.
- Publication
- Cham : Springer, 2020.
- Physical description
- 1 online resource (xiii, 291 pages) : illustrations (some color)
- Series
- Studies in computational intelligence ; v. 833.
Online
Available online
More options
Description
Creators/Contributors
Contents/Summary
- Contents
-
- 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)
- Publisher's summary
-
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)
Subjects
Bibliographic information
- Publication date
- 2020
- Series
- Studies in computational intelligence ; volume 833
- ISBN
- 9783030187644 (electronic bk.)
- 3030187640 (electronic bk.)
- 9783030187637
- 3030187632
- DOI
- 10.1007/978-3-030-18
