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.

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 9, 2010
• 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.
• Public URL: https://durham-repository.worktribe.com/output/1560171