Titlebook: Computational Intelligence in Expensive Optimization Problems; Yoel Tenne,Chi-Keong Goh Book 2010 Springer-Verlag Berlin Heidelberg 2010 a

anatomical

https://doi.org/10.1007/978-3-642-10701-6algorithm; algorithms; computational intelligence; control; data mining; evolution; evolutionary algorithm

丛林

978-3-642-26318-7Springer-Verlag Berlin Heidelberg 2010

Mucosa

指令

1867-4534 tness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporatio978-3-642-26318-7978-3-642-10701-6Series ISSN 1867-4534 Series E-ISSN 1867-4542

粗俗人

A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithmsresented. The main focus areas are the methods of fitness approximation, the working styles of fitness approximation, and the management of the approximation during the optimization process. To conclude, some open questions in this area are discussed.

弯曲道理

etiquette

Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionaryalues in place of metamodels. In addition, to profit of the availability of evaluation or parameterization models of lower fidelity and . cost and/or refinement methods, a multilevel search algorithm relying also on the use of metamodels is presented. The algorithm may optionally operate as hierarch

Keratectomy

Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mappintrate both input space mapping and implicit space mapping through the space mapping optimization of a simple, technology-free wedgecutting problem. We also present tuning space mapping, a powerful methodology, but one that requires extra engineering knowledge of the problem under investigation. To c

CYN

Reducing Function Evaluations Using Adaptively Controlled Differential Evolution with Rough Approxime margin parameter and the congestion parameter according to the success rate of the judgment. The advantage of these improvements is shown by comparing the results obtained by Differential Evolution (DE), DE with the estimated comparison method, adaptively controlled DE with the estimated compariso

expunge

Kriging Is Well-Suited to Parallelize Optimizationely optimize the ., and apply them to the classical Branin-Hoo test-case function. It is finally demonstrated within the covered example that the latter strategies perform as good as the best Latin Hypercubes and Uniform Designs ever found by simulation (2000 designs drawn at random for every . ∈ [1