Using Genetic Programming and Decision Trees for
Generating Structural Descriptions of Four Bar
Mechanisms
A. Ekart, and A. Markus. Artificial Intelligence for Engineering Design,
Analysis and Manufacturing, 17 (3):
205--220(2003)
Abstract
Four bar mechanisms are basic components of many
important mechanical device. The kinematic synthesis of
four bar mechanisms is a difficult design problem.
We present here a novel method that combines the
genetic programming and decision tree learning
methods.
We give a structural description for the class of
mechanisms that produce desired coupler curves. For
finding and characterising feasible regions of the
design space constructive induction is used. Decision
trees constitute the learning engine and the new
features are created by genetic programming.
%0 Journal Article
%1 ekart:2003:AIEDAM
%A Ekart, Aniko
%A Markus, Andras
%D 2003
%J Artificial Intelligence for Engineering Design,
Analysis and Manufacturing
%K algorithms, bar decision four genetic learning machine mechanism programming, synthesis, trees,
%N 3
%P 205--220
%T Using Genetic Programming and Decision Trees for
Generating Structural Descriptions of Four Bar
Mechanisms
%V 17
%X Four bar mechanisms are basic components of many
important mechanical device. The kinematic synthesis of
four bar mechanisms is a difficult design problem.
We present here a novel method that combines the
genetic programming and decision tree learning
methods.
We give a structural description for the class of
mechanisms that produce desired coupler curves. For
finding and characterising feasible regions of the
design space constructive induction is used. Decision
trees constitute the learning engine and the new
features are created by genetic programming.
@article{ekart:2003:AIEDAM,
abstract = {Four bar mechanisms are basic components of many
important mechanical device. The kinematic synthesis of
four bar mechanisms is a difficult design problem.
We present here a novel method that combines the
genetic programming and decision tree learning
methods.
We give a structural description for the class of
mechanisms that produce desired coupler curves. For
finding and characterising feasible regions of the
design space constructive induction is used. Decision
trees constitute the learning engine and the new
features are created by genetic programming.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Ekart, Aniko and Markus, Andras},
biburl = {https://www.bibsonomy.org/bibtex/2ec60879a762894bda30236f9fed260b0/brazovayeye},
interhash = {46a5214e2320ebda0abceb260cd8adf9},
intrahash = {ec60879a762894bda30236f9fed260b0},
issn = {0890-0604},
journal = {Artificial Intelligence for Engineering Design,
Analysis and Manufacturing},
keywords = {algorithms, bar decision four genetic learning machine mechanism programming, synthesis, trees,},
notes = {http://journals.cambridge.org/action/displayJournal?jid=AIE},
number = 3,
pages = {205--220},
timestamp = {2008-06-19T17:39:11.000+0200},
title = {Using Genetic Programming and Decision Trees for
Generating Structural Descriptions of Four Bar
Mechanisms},
volume = 17,
year = 2003
}