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.
Artificial Intelligence for Engineering Design,
Analysis and Manufacturing
pages
1--18
publisher
Cambridge University Press
volume
20
notes
School of Engineering, University of Durham, Durham,
United Kingdom
BAE Systems, Advanced Technology Centre, Filton,
Bristol, United Kingdom
Aerodynamic Methods and Tools, Airbus UK, Filton,
Bristol, United Kingdom
Engineering Design Centre, Engineering Department,
University of Cambridge, Cambridge, United Kingdom
Flat screen design.
%0 Journal Article
%1 Matthews:2006:AIEDAM
%A Matthews, Peter C.
%A Standingford, David W. F.
%A Holden, Carren M. E.
%A Wallace, Ken M.
%D 2006
%I Cambridge University Press
%J Artificial Intelligence for Engineering Design,
Analysis and Manufacturing
%K Data Design Elicitation, GP-HEM, Induction, Knowledge Metamodels Mining, Model SVM, algorithms, demes genetic programming,
%P 1--18
%R doi:10.10170S089006040606001X
%T Learning inexpensive parametric design models using an
augmented genetic programming technique
%V 20
%X 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.
@article{Matthews:2006:AIEDAM,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Matthews, Peter C. and Standingford, David W. F. and Holden, Carren M. E. and Wallace, Ken M.},
biburl = {https://www.bibsonomy.org/bibtex/2b791a20285e194c8e76ae8b970c00e72/brazovayeye},
doi = {doi:10.10170S089006040606001X},
interhash = {39a0f8f5bc7b504c2333d3d5719fa107},
intrahash = {b791a20285e194c8e76ae8b970c00e72},
journal = {Artificial Intelligence for Engineering Design,
Analysis and Manufacturing},
keywords = {Data Design Elicitation, GP-HEM, Induction, Knowledge Metamodels Mining, Model SVM, algorithms, demes genetic programming,},
notes = {School of Engineering, University of Durham, Durham,
United Kingdom
BAE Systems, Advanced Technology Centre, Filton,
Bristol, United Kingdom
Aerodynamic Methods and Tools, Airbus UK, Filton,
Bristol, United Kingdom
Engineering Design Centre, Engineering Department,
University of Cambridge, Cambridge, United Kingdom
Flat screen design.},
pages = {1--18},
publisher = {Cambridge University Press},
size = {18 pages},
timestamp = {2008-06-19T17:46:28.000+0200},
title = {Learning inexpensive parametric design models using an
augmented genetic programming technique},
volume = 20,
year = 2006
}