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Computational Intelligence in Expensive Optimization Problems

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Cover of 'Computational Intelligence in Expensive Optimization Problems'

Table of Contents

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    Book Overview
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    Chapter 1 A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms
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    Chapter 2 A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization
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    Chapter 3 Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionary Algorithms
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    Chapter 4 Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping
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    Chapter 5 Reducing Function Evaluations Using Adaptively Controlled Differential Evolution with Rough Approximation Model
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    Chapter 6 Kriging Is Well-Suited to Parallelize Optimization
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    Chapter 7 Analysis of Approximation-Based Memetic Algorithms for Engineering Optimization
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    Chapter 8 Opportunities for Expensive Optimization with Estimation of Distribution Algorithms
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    Chapter 9 On Similarity-Based Surrogate Models for Expensive Single- and Multi-objective Evolutionary Optimization
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    Chapter 10 Multi-objective Model Predictive Control Using Computational Intelligence
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    Chapter 11 Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression
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    Chapter 12 Differential Evolution with Scale Factor Local Search for Large Scale Problems
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    Chapter 13 Large-Scale Network Optimization with Evolutionary Hybrid Algorithms: Ten Years’ Experience with the Electric Power Distribution Industry
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    Chapter 14 A Parallel Hybrid Implementation Using Genetic Algorithms, GRASP and Reinforcement Learning for the Salesman Traveling Problem
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    Chapter 15 An Evolutionary Approach for the TSP and the TSP with Backhauls
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    Chapter 16 Towards Efficient Multi-objective Genetic Takagi-Sugeno Fuzzy Systems for High Dimensional Problems
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    Chapter 17 Evolutionary Algorithms for the Multi Criterion Minimum Spanning Tree Problem
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    Chapter 18 Loss-Based Estimation with Evolutionary Algorithms and Cross-Validation
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    Chapter 19 Particle Swarm Optimisation Aided MIMO Transceiver Designs
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    Chapter 20 Optimal Design of a Common Rail Diesel Engine Piston
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    Chapter 21 Robust Preliminary Space Mission Design under Uncertainty
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    Chapter 22 Progressive Design Methodology for Design of Engineering Systems
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    Chapter 23 Reliable Network Design Using Hybrid Genetic Algorithm Based on Multi-Ring Encoding
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    Chapter 24 Isolated Word Analysis Using Biologically-Based Neural Networks
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    Chapter 25 A Distributed Evolutionary Approach to Subtraction Radiography
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    Chapter 26 Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance
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Title
Computational Intelligence in Expensive Optimization Problems
Published by
Springer Berlin Heidelberg, January 2010
DOI 10.1007/978-3-642-10701-6
ISBNs
978-3-64-210700-9, 978-3-64-210701-6
Authors

Yoel Tenne, Chi-Keong Goh

Editors

Tenne, Yoel, Goh, Chi-Keong

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 3%
United Kingdom 2 3%
Brazil 2 3%
Malaysia 1 1%
Germany 1 1%
Vietnam 1 1%
Canada 1 1%
Japan 1 1%
Unknown 69 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 31%
Researcher 14 18%
Student > Master 9 11%
Professor 7 9%
Student > Doctoral Student 4 5%
Other 11 14%
Unknown 10 13%
Readers by discipline Count As %
Engineering 26 33%
Computer Science 25 31%
Mathematics 2 3%
Agricultural and Biological Sciences 2 3%
Business, Management and Accounting 2 3%
Other 8 10%
Unknown 15 19%