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Learning inexpensive parametric design models using an augmented genetic programming technique

Published online by Cambridge University Press:  10 February 2006

PETER C. MATTHEWS ,
DAVID W.F. STANDINGFORD ,
CARREN M.E. HOLDEN  and
KEN M. WALLACE
PETER C. MATTHEWS
Affiliation:
School of Engineering, University of Durham, Durham, United Kingdom
DAVID W.F. STANDINGFORD
Affiliation:
BAE Systems, Advanced Technology Centre, Filton, Bristol, United Kingdom
CARREN M.E. HOLDEN
Affiliation:
Aerodynamic Methods and Tools, Airbus UK, Filton, Bristol, United Kingdom
KEN M. WALLACE
Affiliation:
Engineering Design Centre, Engineering Department, University of Cambridge, Cambridge, United Kingdom
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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 heuristics extraction method, is described and illustrated by means of a design case study.

Keywords

Data MiningDesign Model InductionGenetic ProgrammingKnowledge ElicitationMetamodels
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 1 , February 2006 , pp. 1 - 18
Copyright
2006 Cambridge University Press

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