Computational Intelligence in Expensive Optimization Problems (Record no. 111771)

000 -LEADER
fixed length control field 05901nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-642-10701-6
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220084529.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 100309s2010 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642107016
-- 978-3-642-10701-6
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-642-10701-6
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA329-348
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA640-643
072 #7 - SUBJECT CATEGORY CODE
Subject category code TBJ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT003000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Tenne, Yoel.
Relator term editor.
245 10 - TITLE STATEMENT
Title Computational Intelligence in Expensive Optimization Problems
Medium [electronic resource] /
Statement of responsibility, etc edited by Yoel Tenne, Chi-Keong Goh.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg,
-- 2010.
300 ## - PHYSICAL DESCRIPTION
Extent 800p. 270 illus.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Adaptation Learning and Optimization,
International Standard Serial Number 1867-4534 ;
Volume number/sequential designation 2
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Techniques for Resource-Intensive Problems -- A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms -- A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization -- Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionary Algorithms -- Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping -- Reducing Function Evaluations Using Adaptively Controlled Differential Evolution with Rough Approximation Model -- Kriging Is Well-Suited to Parallelize Optimization -- Analysis of Approximation-Based Memetic Algorithms for Engineering Optimization -- Opportunities for Expensive Optimization with Estimation of Distribution Algorithms -- On Similarity-Based Surrogate Models for Expensive Single- and Multi-objective Evolutionary Optimization -- Multi-objective Model Predictive Control Using Computational Intelligence -- Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression -- Techniques for High-Dimensional Problems -- Differential Evolution with Scale Factor Local Search for Large Scale Problems -- Large-Scale Network Optimization with Evolutionary Hybrid Algorithms: Ten Years’ Experience with the Electric Power Distribution Industry -- A Parallel Hybrid Implementation Using Genetic Algorithms, GRASP and Reinforcement Learning for the Salesman Traveling Problem -- An Evolutionary Approach for the TSP and the TSP with Backhauls -- Towards Efficient Multi-objective Genetic Takagi-Sugeno Fuzzy Systems for High Dimensional Problems -- Evolutionary Algorithms for the Multi Criterion Minimum Spanning Tree Problem -- Loss-Based Estimation with Evolutionary Algorithms and Cross-Validation -- Real-World Applications -- Particle Swarm Optimisation Aided MIMO Transceiver Designs -- Optimal Design of a Common Rail Diesel Engine Piston -- Robust Preliminary Space Mission Design under Uncertainty -- Progressive Design Methodology for Design of Engineering Systems -- Reliable Network Design Using Hybrid Genetic Algorithm Based on Multi-Ring Encoding -- Isolated Word Analysis Using Biologically-Based Neural Networks -- A Distributed Evolutionary Approach to Subtraction Radiography -- Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance.
520 ## - SUMMARY, ETC.
Summary, etc In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc. Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in such settings. This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems. Topics covered include: Dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic. Reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance). Frameworks for optimization (model management, complexity control, model selection). Parallelization of algorithms (implementation issues on clusters, grids, parallel machines). Incorporation of expert systems and human-system interface. Single and multiobjective algorithms. Data mining and statistical analysis. Analysis of real-world cases (such as multidisciplinary design optimization). The edited book provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields. As such, it is a comprehensive reference for researchers, practitioners, and advanced-level students interested in both the theory and practice of using computational intelligence for expensive optimization problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering mathematics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Appl.Mathematics/Computational Methods of Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Applications of Mathematics.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Goh, Chi-Keong.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642107009
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Adaptation Learning and Optimization,
-- 1867-4534 ;
Volume number/sequential designation 2
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-10701-6
912 ## -
-- ZDB-2-ENG

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