Produktbild: Computational Intelligence in Expensive Optimization Problems
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Computational Intelligence in Expensive Optimization Problems

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.05.2012

Abbildungen

270 illus., schwarz-weiss Illustrationen

Herausgeber

Yoel Tenne + weitere

Verlag

Springer Berlin

Seitenzahl

800

Maße (L/B/H)

23,5/15,5/4,1 cm

Gewicht

1130 g

Auflage

2010

Sprache

Englisch

ISBN

978-3-642-26318-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.05.2012

Abbildungen

270 illus., schwarz-weiss Illustrationen

Herausgeber

Verlag

Springer Berlin

Seitenzahl

800

Maße (L/B/H)

23,5/15,5/4,1 cm

Gewicht

1130 g

Auflage

2010

Sprache

Englisch

ISBN

978-3-642-26318-7

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: GPSR Kontakt

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  • Produktbild: Computational Intelligence in Expensive Optimization Problems
  • 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.