000 05901nam a22005055i 4500
001 978-3-642-10701-6
003 DE-He213
005 20140220084529.0
007 cr nn 008mamaa
008 100309s2010 gw | s |||| 0|eng d
020 _a9783642107016
_9978-3-642-10701-6
024 7 _a10.1007/978-3-642-10701-6
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aTenne, Yoel.
_eeditor.
245 1 0 _aComputational Intelligence in Expensive Optimization Problems
_h[electronic resource] /
_cedited by Yoel Tenne, Chi-Keong Goh.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _a800p. 270 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdaptation Learning and Optimization,
_x1867-4534 ;
_v2
505 0 _aTechniques 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 _aIn 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 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aMathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aApplications of Mathematics.
700 1 _aGoh, Chi-Keong.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642107009
830 0 _aAdaptation Learning and Optimization,
_x1867-4534 ;
_v2
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-10701-6
912 _aZDB-2-ENG
999 _c111771
_d111771