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Inexact information aided, low‐cost, distributed genetic algorithms for aerodynamic shape optimization

https://doi.org/10.1002/fld.575
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-95665-2_12openalex-snapshot
A Structured Distributed Genetic Algorithm for Function Optimization
inferredcites →https://doi.org/10.1007/978-3-662-07418-3openalex-snapshot
Genetic Algorithms + Data Structures = Evolution Programs
inferredcites →https://doi.org/10.1007/bfb0056902openalex-snapshot
Hybrid distributed real-coded genetic algorithms
inferredcites →https://doi.org/10.1016/s0045-7825(97)00285-5openalex-snapshot
Solution of flow problems using unstructured grids on distributed memory platforms
inferredcites →https://doi.org/10.1016/s0376-0421(01)00019-7openalex-snapshot
Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence
inferredcites →https://doi.org/10.1080/174159701088027771openalex-snapshot
Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters
inferredcites →https://doi.org/10.1109/5.58326openalex-snapshot
Networks for approximation and learning
inferredcites →https://doi.org/10.2514/3.9789openalex-snapshot
Viscous-inviscid analysis of transonic and low Reynolds number airfoils
inferredcites →https://doi.org/10.2514/6.1997-1852openalex-snapshot
Supervised parallel genetic algorithms in aerodynamic optimisation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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