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Learning inexpensive parametric design models using an augmented genetic programming technique

Matthews, P.C.; Standingford, D.W.F.; Holden, C.M.E.; Wallace, K.M.

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Authors

D.W.F. Standingford

C.M.E. Holden

K.M. Wallace



Abstract

Previous applications of Genetic Programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters (e.g. geometrical parameters) to a single design objective (e.g. weight). In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands `cooperate', simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP-HEM (Genetic Programming Heuristics Extraction Method), is described and illustrated by means of a design case study.

Citation

Matthews, P., Standingford, D., Holden, C., & Wallace, K. (2006). Learning inexpensive parametric design models using an augmented genetic programming technique. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 20(1), 1-18. https://doi.org/10.1017/s089006040606001x

Journal Article Type Article
Publication Date 2006-02
Deposit Date Aug 14, 2008
Publicly Available Date Mar 28, 2024
Journal Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Print ISSN 0890-0604
Electronic ISSN 1469-1760
Publisher Cambridge University Press
Peer Reviewed Peer Reviewed
Volume 20
Issue 1
Pages 1-18
DOI https://doi.org/10.1017/s089006040606001x
Keywords Genetic programming, Knowledge elicitation, Design model induction, Meta-models, Data mining.

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