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High-Performance Simulation-Based Optimization

Overview of attention for book
Cover of 'High-Performance Simulation-Based Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Infill Criteria for Multiobjective Bayesian Optimization
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    Chapter 2 Many-Objective Optimization with Limited Computing Budget
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    Chapter 3 Multi-objective Bayesian Optimization for Engineering Simulation
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    Chapter 4 Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration
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    Chapter 5 Optimization and Visualization in Many-Objective Space Trajectory Design
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    Chapter 6 Simulation Optimization Through Regression or Kriging Metamodels
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    Chapter 7 Towards Better Integration of Surrogate Models and Optimizers
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    Chapter 8 Surrogate-Assisted Evolutionary Optimization of Large Problems
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    Chapter 9 Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems
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    Chapter 10 Open Issues in Surrogate-Assisted Optimization
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    Chapter 11 A Parallel Island Model for Hypervolume-Based Many-Objective Optimization
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    Chapter 12 Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors
Attention for Chapter 2: Many-Objective Optimization with Limited Computing Budget
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Citations

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Chapter title
Many-Objective Optimization with Limited Computing Budget
Chapter number 2
Book title
High-Performance Simulation-Based Optimization
Published by
Springer, Cham, June 2019
DOI 10.1007/978-3-030-18764-4_2
Book ISBNs
978-3-03-018763-7, 978-3-03-018764-4
Authors

Kalyan Shankar Bhattacharjee, Hemant Kumar Singh, Tapabrata Ray

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 33%
Unknown 2 67%
Readers by discipline Count As %
Engineering 1 33%
Unknown 2 67%

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