A Re-Examination of a Real World Blood Flow Modeling Problem Using Context-Aware Crossover

  • Hammad Majeed
  • Conor Ryan
Part of the Genetic and Evolutionary Computation book series (GEVO)


This chapter describes context-aware crossover. This is an improved crossover technique for GP which always swaps subtrees into their best possible context in a parent. We show that this style of crossover is considerably more constructive than the standard method, and present several experiments to demonstrate how it operates, and how well it performs, before applying the technique to a real world application, the Blood Flow Modeling Problem.


Genetic Program Real World Application Crossover Operator Good Individual Linear Scaling 
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  1. Azad, R. Muhammad Atif, Ansari, Ali R., Ryan, Conor, Walsh, Michael, and McGloughlin, Tim (2004). An evolutionary approach to wall sheer stress prediction in a grafted artery. Applied Soft Computing, 4(2):139–148.CrossRefGoogle Scholar
  2. Azad, R. Muhammad Atif and Ryan, Conor (2005). An examination of simultaneous evolution of grammars and solutions. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice HI, volume 9 of Genetic Programming, chapter 10, pages 141–158. Kluwer, Ann Arbor.Google Scholar
  3. Azad, Raja Muhammad Atif (2003). A Position Independent Representation for Evolutionary Automatic Programming Algorithms — The Chorus System. PhD thesis, University of Limerick, Ireland.Google Scholar
  4. Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E., and Francone, Frank D. (1998). Genetic Programming — An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA.MATHGoogle Scholar
  5. D’haeseleer, Patrik (1994). Context preserving crossover in genetic programming. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence, volume 1, pages 256–261, Orlando, Florida, USA. IEEE Press.CrossRefGoogle Scholar
  6. Ethier, C.R., Steinman, D.A., Zhang, X., Karpik, S.R., and Ohja, M. (1998). Flow waveform effects on end-to-side anastomotic flow patterns. Journal of BioMechanics, 31(7):609–617.CrossRefGoogle Scholar
  7. Hengproprohm, S. and Chongstitvatana, P. (2001). Selective crossover in genetic programming. In ISCIT International Symposium on Communications and Information Technologies, ChiangMai Orchid, ChiangMai Thailand.Google Scholar
  8. Ito, Takuya, Iba, Hitoshi, and Sato, Satoshi (1998a). Depth-dependent crossover for genetic programming. In Proceedings of the 1998 IEEE World Congress on Computational Intelligence, pages 775–780, Anchorage, Alaska, USA. IEEE Press.CrossRefGoogle Scholar
  9. Ito, Takuya, Iba, Hitoshi, and Sato, Satoshi (1998b). Non-destructive depth-dependent crossover for genetic programming. In Banzhaf, Wolfgang, Poli, Riccardo, Schoenauer, Marc, and Fogarty, Terence C., editors, Proceedings of the First European Workshop on Genetic Programming, volume 1391 of LNCS, pages 71–82, Paris. Springer-Verlag.Google Scholar
  10. Keijzer, Maarten (2003). Improving symbolic regression with interval arithmetic and linear scaling. In Ryan, Conor, Soule, Terence, Keijzer, Maarten, Tsang, Edward, Poli, Riccardo, and Costa, Ernesto, editors, Genetic Programming, Proceedings of EuroGP’2003, volume 2610 of LNCS, pages 70–82, Essex. Springer-Verlag.Google Scholar
  11. Majeed, Hammad and Ryan, Conor (2006a). A less destructive, context-aware crossover for gp. In Genetic Programming 9th European Conference, EuroGP 2006, Proceedings. Springer-Verlag.Google Scholar
  12. Majeed, Hammad and Ryan, Conor (2006b). Using context-aware crossover to improve the performance and lower the cost of gp. In Sumitted to GECCO 2006: Proceedings of the 2006 conference on Genetic and evolutionary computation.Google Scholar
  13. Ohja, M., Cobbold, R.S., and Johnston, K.W. (1994). Influence of angle on wall shear stress distribution for an end-to-side anastomosis. Journal of Vascular Surgery, 19:1067–1073.Google Scholar
  14. Poli, Riccardo and Langdon, William B. (1998a). On the ability to search the space of programs of standard, one-point and uniform crossover in genetic programming. Technical Report CSRP-98-7, University of Birmingham, School of Computer Science. Presented at GP-98.Google Scholar
  15. Poli, Riccardo and Langdon, William B. (1998b). On the search properties of different crossover operators in genetic programming. In Koza, John R., Banzhaf, Wolfgang, Chellapilla, Kumar, Deb, Kalyanmoy, Dorigo, Marco, Fogel, David B., Garzon, Max H., Goldberg, David E., Iba, Hitoshi, and Riolo, Rick, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 293–301, University of Wisconsin, Madison, Wisconsin, USA. Morgan Kaufmann.Google Scholar
  16. Tackett, Walter Alden (1994). Recombination, Selection, and the Genetic Construction of Computer Programs. PhD thesis, University of Southern California, Department of Electrical Engineering Systems, USA.Google Scholar
  17. Yuen, Chi Chung (2004). Selective crossover using gene dominance as an adaptive strategy for genetic programming. Msc intelligent systems, University College, London, UK.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Hammad Majeed
    • 1
  • Conor Ryan
    • 1
  1. 1.Department of Computer Science and Information SystemsUniversity of LimerickIreland

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