Hostname: page-component-7dd5485656-wxk4p Total loading time: 0 Render date: 2025-10-30T22:31:57.927Z Has data issue: false hasContentIssue false
  • English
  • Français

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Published online by Cambridge University Press:  12 February 2004

ANIKÓ EKÁRT  and
ANDRÁS MÁRKUS
ANIKÓ EKÁRT
Affiliation:
Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary
ANDRÁS MÁRKUS
Affiliation:
Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary
Get access Rights & Permissions [Opens in a new window]

Abstract

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Keywords

Decision TreesFour Bar Mechanism SynthesisGenetic ProgrammingMachine Learning

Information

Type
Research Article
Information
AI EDAM , Volume 17 , Issue 3 , August 2003 , pp. 205 - 220
Copyright
© 2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Bloedorn, E. & Michalski, R.S. (1998). Data-driven constructive induction. IEEE Expert and Intelligent Systems 13(2), 30–37.CrossRefGoogle Scholar
Blum, A.L. & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271.Google Scholar
Bose, A., Gini, M., & Riley, D. (1997). A case-based approach to planar linkage design. Artificial Intelligence in Engineering 11, 107–119.CrossRefGoogle Scholar
Ceccarelli, M. & Vinciguerra, A. (2000). Approximate four-bar circle-tracing mechanisms: Classical and new synthesis. Mechanism and Machine Theory 35, 1579–1599.CrossRefGoogle Scholar
Chironis, N.P. (1991). Mechanisms and Mechanical Devices Sourcebook. New York: McGraw–Hill.
Cramer, N. (1985). A representation for the adaptive generation of simple sequential programs. Proc. Int. Conf. Genetic Algorithms and Their Applications, pp. 183–187.
Daida, J., Bertram, R., Stanhope, S., Khoo, J., Chaudhary, S., Chaudri, O., & Polito III, J. (2001). What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines 2(2), 165–191.CrossRefGoogle Scholar
Ekárt, A. (2001). Genetic programming: New performance improving methods and applications. PhD Thesis. Eötvös Loránd University, Budapest.
Ekárt, A. & Márkus, A. (1999). Decision trees and genetic programming in synthesis of four bar mechanisms. In Life Cycle Approaches to Production Systems. Proc. Advanced Summer Institute—ASI'99, pp. 201–208.
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. New York: Addison–Wesley.
Hoeltzel, D.A. & Chieng, W.-H. (1990). Pattern matching synthesis as an approach to mechanism design. ASME Journal of Mechanical Design 112, 190–199.Google Scholar
Hrones, J.A. & Nelson, G.L. (1951). Analysis of the Four Bar Linkage. New York: Wiley.
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
Mariappan, J. & Krishnamurty, S. (1996). A generalized exact gradient method for mechanism synthesis. Mechanisms and Machine Theory 31, 413–421.CrossRefGoogle Scholar
Matheus, C.J. & Rendell, L. (1989). Constructive induction on decision trees. Proc. IJCAI, pp. 645–650. San Francisco, CA: Morgan Kaufmann.
Norton, R.L. (1992). Design of Machinery. New York: McGraw–Hill.
Pagallo, G. (1989). Learning DNF by decision trees. Proc. IJCAI, pp. 639–644. San Francisco, CA: Morgan Kaufmann.
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann.
Sandor, G.N. & Erdman, A.G. (1984). Advanced Mechanism Design: Analysis and Synthesis, Vol. 2. Englewood Cliffs, NJ: Prentice–Hall.
Vancsay, G. (2000). The application of genetic algorithms in mechanical design. PhD Thesis. Technical University of Budapest.
Waldron, K.J. & Kinzel, G.L. (1999). Kinematics, Dynamics and Design of Machinery. New York: Wiley.
Wnek, J. & Michalski, R.S. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning 14, 139–168.Google Scholar