Computational intelligence in expensive optimization problems
著者
書誌事項
Computational intelligence in expensive optimization problems
(Adaptation, learning, and optimization / series editors in chief, Meng-Hiot Lin, Yew-Soon Ong, v. 2)
Springer, c2010
- : hardcover
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
「Nielsen BookData」 より