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An Indirect Approach to the Three-Dimensional Multi-pipe Routing Problem

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Genetic Programming (EuroGP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6021))

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Abstract

This paper explores an indirect approach to the Three- dimensional Multi-pipe Routing problem. Variable length pipelines are built by letting a virtual robot called a turtle navigate through space, leaving pipe segments along its route. The turtle senses its environment and acts in accordance with commands received from heuristics currently under evaluation. The heuristics are evolved by a Gene Expression Programming based Learning Classifier System. The suggested approach is compared to earlier studies using a direct encoding, where command lines were evolved directly by genetic algorithms. Heuristics generating higher quality pipelines are evolved by fewer generations compared to the direct approach, however the evaluation time is longer and the search space is more complex. The best evolved heuristic is short and simple, builds modular solutions, exhibits some degree of generalization and demonstrates good scalability on test cases similar to the training case.

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References

  1. Eiben, A., Schoenauer, M.: Evolutionary computing. Arxiv preprint cs/0511004 (2005)

    Google Scholar 

  2. Stanley, K., Miikkulainen, R.: A Taxonomy for Artificial Embryogeny. Artificial Life 9(2), 93–130 (2003)

    Article  Google Scholar 

  3. Norvig, P., Russell, S.: Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs (2003)

    Google Scholar 

  4. Ito, T.: A genetic algorithm approach to piping route path planning. Journal of Intelligent Manufacturing 10(1), 103–114 (1999)

    Article  Google Scholar 

  5. Ito, T.: Route Planning Wizard: Basic Concept and Its Implementation. In: Hendtlass, T., Ali, M. (eds.) IEA/AIE 2002. LNCS (LNAI), vol. 2358, pp. 547–556. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  6. Soltani, A., Tawfik, H., Goulermas, J., Fernando, T.: Path planning in construction sites: performance evaluation of the Dijkstra, A*, and GA search algorithms. Advanced Engineering Informatics 16(4), 291–303 (2002)

    Article  Google Scholar 

  7. Kim, D., Corne, D., Ross, P.: Industrial plant pipe-route optimisation with genetic algorithms. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 1012–1021. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  8. Fan, J., Ma, M., Yang, X.: Path Planning in Pipe System Based on Coevolution[for aero-engines]. Hangkong Dongli Xuebao/Journal of Aerospace Power 19(5), 593–597 (2004)

    Google Scholar 

  9. Zhu, D., Latombe, J.: Pipe routing-path planning (with many constraints). In: Proceedings of 1991 IEEE International Conference on Robotics and Automation, pp. 1940–1947 (1991)

    Google Scholar 

  10. Sandurkar, S., Chen, W.: GAPRUSgenetic algorithms based pipe routing using tessellated objects. Computers in Industry 38(3), 209–223 (1999)

    Article  Google Scholar 

  11. Wang, H., Zhao, C., Yan, W., Feng, X.: Three-dimensional Multi-pipe Route Optimization Based on Genetic Algorithms. International Federation for Information Processing-publications-IFIP 207, 177 (2006)

    Article  Google Scholar 

  12. Park, J., Storch, R.: Pipe-routing algorithm development: case study of a ship engine room design. Expert Systems with Applications 23(3), 299–309 (2002)

    Article  Google Scholar 

  13. Burke, E., Hyde, M., Kendall, G.: Evolving bin packing heuristics with genetic programming. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, p. 860. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Burke, E., Hyde, M., Kendall, G., Woodward, J.: A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. Technical report, Technical report, University of Nottingham, Dept. of Computer Science (2008)

    Google Scholar 

  15. Allen, S., Burke, E., Hyde, M., Kendall, G.: Evolving reusable 3d packing heuristics with genetic programming. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 931–938. ACM, New York (2009)

    Chapter  Google Scholar 

  16. Furuholmen, M., Glette, K., Hovin, M., Torresen, J.: Coevolving Heuristics for The Distributors Pallet Packing Problem. In: Proceedings of the IEEE Congress on Evolutionary Computation (2009)

    Google Scholar 

  17. Tay, J., Ho, N.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54(3), 453–473 (2008)

    Article  Google Scholar 

  18. Dimopoulos, C., Zalzala, A.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software 32(6), 489–498 (2001)

    Article  MATH  Google Scholar 

  19. Jakobovic, D., Budin, L.: Dynamic Scheduling with Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, p. 73. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  20. Furuholmen, M., Glette, K., Hovin, M., Torresen, J.: Scalability, generalization and coevolution–experimental comparisons applied to automated facility layout planning. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 691–698. ACM, New York (2009)

    Chapter  Google Scholar 

  21. Floreano, D., Nolfi, S.: Evolutionary Robotics. Springer Handbook of Robotics (2008)

    Google Scholar 

  22. Lee, W., Hallam, J., Lund, H.: Applying genetic programming to evolve behavior primitives andarbitrators for mobile robots. In: IEEE International Conference on Evolutionary Computation 1997, pp. 501–506 (1997)

    Google Scholar 

  23. Ebner, M.: Evolution of a control architecture for a mobile robot. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 303–310. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  24. Koza, J.: Evolution of subsumption using genetic programming. In: Toward a Practice of Autonomous Systems, Proceedings of the First European Conference on Artificial Life, pp. 110–119. MIT, Cambridge (1992)

    Google Scholar 

  25. Furuholmen, M., Hovin, M., Torresen, J., Glette, K.: Continuous Adaptation in Robotic Systems by Indirect Online Evolution. In: Proceedings of Learning and Adaptive Behaviors for Robotic Systems, Lab-Rs 2008, Edinburgh, United Kingdom, August 6-8 (2008)

    Google Scholar 

  26. Furuholmen, M., Glette, K., Torresen, J., Hovin, M.: Indirect Online Evolution - A Conceptual Framework for Adaptation in industrial Robotic Systems. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 165–176. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  27. Hornby, G., Lipson, H., Pollack, J.: Generative representations for the automated design of modular physical robots. IEEE Transactions on Robotics and Automation 19(4), 703–719 (2003)

    Article  Google Scholar 

  28. Kowaliw, T., Grogono, P., Kharma, N.: The evolution of structural design through artificial embryogeny. In: Proceedings of the IEEE First International Symposium on Artificial Life (2007)

    Google Scholar 

  29. Abelson, H., Disessa, A.: Turtle geometry: The computer as a medium for exploring mathematics. The MIT Press, Cambridge (1986)

    Google Scholar 

  30. Holland, J., Reitman, J.: Cognitive systems based on adaptive algorithms. ACM SIGART Bulletin 49 (1977)

    Google Scholar 

  31. Dorigo, M., Schnepf, U.: Genetics-based machine learning and behavior-based robotics: a new synthesis. IEEE Transactions on Systems Man and Cybernetics 23(1), 141–154 (1993)

    Article  Google Scholar 

  32. Wilson, S.: Classifier conditions using gene expression programming. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds.) IWLCS 2006 and IWLCS 2007. LNCS (LNAI), vol. 4998, pp. 206–217. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  33. Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Arxiv preprint cs.AI/0102027 (2001)

    Google Scholar 

  34. Nordin, P., Banzhaf, W., Brameier, M., et al.: Evolution of a world model for a miniature robot using genetic programming. Robotics and Autonomous Systems 25(1), 105–116 (1998)

    Article  Google Scholar 

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Furuholmen, M., Glette, K., Hovin, M., Torresen, J. (2010). An Indirect Approach to the Three-Dimensional Multi-pipe Routing Problem. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds) Genetic Programming. EuroGP 2010. Lecture Notes in Computer Science, vol 6021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12148-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-12148-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12147-0

  • Online ISBN: 978-3-642-12148-7

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