Skip to main content

A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6040))

Abstract

In the current paper is presented the application of a Gene Expression Programming Environment in modeling the fatigue behavior of composite materials. The environment was developed using the JAVA programming language, and is an implementation of a variation of Gene Expression Programming. Gene Expression Programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based Genetic Programming techniques. In order to evaluate the performance of the presented environment, we tested it in fatigue modeling of composite materials.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Koza, J.R.: Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems Technical Report STAN-TR-CS 1314, Stanford University (1990)

    Google Scholar 

  2. Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  3. Winkler, S., Affenzeller, M., Wagner, S.: Identifying Nonlinear Model Structures Using Genetic Programming Techniques. In: Cybernetics and Systems 2004. Austrian Society for Cybernetic Studies, pp. 689–694 (2004)

    Google Scholar 

  4. Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)

    MATH  MathSciNet  Google Scholar 

  5. Lopez, H.S., Weinert, W.R.: EGIPSYS: An Enhanced Gene Expression Programming Approach for Symbolic Regression Problems. International Journal of Applied Mathematics in Computer Science 14(3), 375–384 (2004)

    Google Scholar 

  6. Margny, M.H., El-Semman, I.E.: Extracting Logical Classification Rules with Gene Expression Programming: Micro array case study. In: AIML 2005 Conference, Cairo, Egypt, December 19-21 (2005)

    Google Scholar 

  7. Dehuri, S., Cho, S.B.: Multi-Objective Classification Rule Mining Using Gene Expression Programming. Third International Conference on Convergence and Hybrid Information Technology (2008)

    Google Scholar 

  8. Philippidis, T.P., Vassilopoulos, A.P.: Complex stress state effect on fatigue life of GRP laminates. Part I, experimental. Int. J. Fatigue 24, 813–823 (2002)

    Article  Google Scholar 

  9. Nijssen, R.P.L.: OptiDAT – fatigue of wind turbine materials database, http://www.kc-wmc.nl/optimat_blades/index.htm

  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  11. Darwin, C.: On the Origin of Species (1859)

    Google Scholar 

  12. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  13. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Antoniou, M.A., Georgopoulos, E.F., Theofilatos, K.A., Vassilopoulos, A.P., Likothanassis, S.D. (2010). A Gene Expression Programming Environment for Fatigue Modeling of Composite Materials. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12842-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12841-7

  • Online ISBN: 978-3-642-12842-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics