Advertisement

Evolutionary Computing within Grid Environment

  • Ashutosh Tiwari
  • Gokop Goteng
  • Rajkumar Roy
Part of the Studies in Computational Intelligence book series (SCI, volume 66)

Evolutionary computing (EC) techniques such as genetic algorithms (GA), genetic programming (GP), evolutionary programming (EP) and evolution strategies (ES) mimic nature through natural selection to perform complex optimisation processes. Grid-enabled environment provides suitable framework for EC techniques due to its computational and data capabilities. In addition, the semantic and knowledge Grids aid in the design search and exploration for multi-objective optimisation tasks. This chapter explores some problem solving environments such as Geodise (Grid-Enabled Optimisation Design Search for Engineering), FIPER (Federated Intelligent Product Environment), SOCER (Service-Oriented Concurrent Environment), DAME (Distributed Aircraft Maintenance Environment) and Globus toolkit to demonstrate how EC techniques can be performed more efficiently within a Grid environment. Service-oriented and autonomic computing features of Grid are discussed to highlight how EC algorithms can be published as services by service providers and used by service requestors dynamically. Grid computational steering and visualisation are features that can be used for real-time tuning of parameters and visual display of optimal solutions. This chapter demonstrates that grid-enabled evolutionary computing marks the future of optimisation techniques.

Keywords

Grid Computing Service Requestor Grid Environment Grid Service Evolutionary Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbas, Ahmar. Grid Computing: A Practical Guide to Technology and Applications. USA: Charles River Media, Inc. (2004).Google Scholar
  2. 2.
    Agarwal, Manish and Parashar, Manish. Enabling Autonomic Compositions in Grid Environments. Proceedings of the Fourth International Workshop on Grid Computing (GRID’03). USA: IEEE Computer Society. (2003).Google Scholar
  3. 3.
    Back, Thomas; Hammel, Ulrich, and Schwefel, Hans-Paul. Evolutionary Computation: Comments on the History and Current State. IEEE Transactions on Evolutionary Computation. (1997) 13-17.Google Scholar
  4. 4.
    Cannataro, Mario; Comito, Carmela, and Guzzo, Antonella Veltri Pierangelo. Integrating Ontology and Workflow in PROTEUS, a Grid-Based Problem Solving Environment for Bioinformatics. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04). USA: IEEE Computer Society. (2004).Google Scholar
  5. 5.
    Cannataro, Mario and Talia, Domenico. Semantics and Knowledge Grids: Building the Next-Generation Grid. IEEE Intelligent Systems. (2004) 56-63.Google Scholar
  6. 6.
    Cannataro, Mario and Talia, Domenico. Towards the Next-Generation Grid: A Pervasive Environment for Knowledge-Based Computing. Proceedings of the International Conference on Information Technology: Computers and Communications (ITCC’03). USA: IEEE Computer Society. (2003).Google Scholar
  7. 7.
    Cao, Junwei; Jarvis, Stephen A., and Saini, Subhash Nudd Graham R. GridFlow: Workflow Management for Grid Computing. Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID’03). USA: IEEE Computer Society. (2003).Google Scholar
  8. 8.
    Cvetkovic, Dragan and Parmee, Ian. AgentBased Support within an Interactive Evolutionary Design System. Parmee, I. C. Adaptive Computing in Design and Manufacture. 5th ed. London, UK: Springer-Verlag London Limited, (2002): 355-367.Google Scholar
  9. 9.
    Dai, Yuan-Shun and Wang, Xiao-Shun. Optimal Resource Allocation on Grid Systems for Maximizing Service Relaibility using a Genetic Algorithm. Elsevier: Reliability Engineering and System Safety. (2005).Google Scholar
  10. 10.
    Deb, Kalyanmoy. Multi-Objective Optimisation using Evolutionary Algorithms. England, UK: John Wiley & Sons Ltd. (2001).Google Scholar
  11. 11.
    Eddy, John and Lewis, Kemper. Multidimensional Design Visualization in Multiobjective Optimization. 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. USA: AIAA Inc. (2002).Google Scholar
  12. 12.
    Foster, Ian; Insley, Joseph; Laszewski, Gregor von; Kesselman, Carl, and Thiebaux, Marcus. Distance Visualization: Data Exploration on the Grid. IEEE Computer Society. (1999) 26-43. CODEN: 0018-9162/99.Google Scholar
  13. 13.
    Foster, Ian and Kesselman, Carl. The Grid: Blueprint for a New Computer Infrastructure. San Francisco, USA: Morgan Kaufman Publishers Inc. (1999).Google Scholar
  14. 14.
    Fraga, E. S. and Zilinskas, A. Experience with Hybrid Evolutionary/Local Optimization for Process Design. Parmee, I. C. Adaptive Computing in Design and Manufacture. 5th ed. London, UK: Springer-Verlag London Limited. (2002) 53-64.Google Scholar
  15. 15.
    Gil, Yolanda, Deelman, Ewa, Blythe, Jim, Kesselman, Carl and Tangmunarunkit, Hongsuda. Artificial Intelligence and Grids: Workflow Planning and Beyond. IEEE Intelligent Systems. (2004) 26-33.Google Scholar
  16. 16.
    Goux, Jean-Pierre, Kulkarni, Sanjeev, Yoder, Michael and Linderoth, Jeff. Master-Worker: An Enabling Framework for Applications on the Computational Grid. Kluwer Academic Publishers: Cluster Computing. (2001) 463-70.Google Scholar
  17. 17.
    Joshi, Anupam, Weerawarana, Sanjiva, Ramakrishnan, Narendran, Houstis, Elias N. and Rice, John R. Neuro-Fuzzy Support for Problem-Solving Environments: A Step Toward Automated Solution of PDEs. IEEE Computational Science & Engineering: Neural Networks Problem Solving Environments. (1996) 44-56.Google Scholar
  18. 18.
    Kacsuk, P., Goyeneche, A., Delaitre, T.; Kiss, T., Farkas, Z. and Bocko, T. High-Level Grid Application Environment to Use Legacy Codes as OGSA Grid Services. Proceedings of the Fifth IEEE/ACM International Workshop on Grid Computing (GRID’04). USA: IEEE Computer Society. (2004).Google Scholar
  19. 19.
    Kodiyalam, Srinivas; Yang, R.J., and Gu, Lei. High Performance Com- puting & Rapid Visualization for Design Steering in MDO. 44th AIAA/ASME/ASCE/AHS Structures, Structural Dynamics, and Materials Conference. USA : AIAA Inc. (2003).Google Scholar
  20. 20.
    Li, Yuhua and Lu, Zhengding. Ontology-Based Universal Knowledge Grid: Enabling Knowledge Discovery and Integration on the Grid. Proceedings of the 2004 IEEE International Conference on Services Computing (SCC 04). USA: IEEE Computer Society. (2004).Google Scholar
  21. 21.
    Masher, M. L. and Garza, A. Gomez de Silva. Adapting Problem Specifications and Design Solutions Using Co-evolution. Parmee, I. C. Adaptive Computing in Design and Manufacture. 5th ed. London, UK: Springer-Verlag London Limited. (2002)257-271.Google Scholar
  22. 22.
    Naik, Vijay K.; Sivasubramanian, Swaminathan, and Krishnan, Sriram. Adap- tive Resource Sharing in a Web Services Environment. IFIP International Federation for Information Processing. (2004) 311-330.Google Scholar
  23. 23.
    Ong, M.; Alkarouri, M.; Ren, X.; Allan, G.; Kadirkamanathan, V.; Thompson, H. A., and Fleming, P. J. Grid-Based Decision Support with Pro-Active Mobile Computing. Proceedings of the 2005 IEEE International Conference on Services Computing (SCC’05). USA: IEEE Computer Society. (2005).Google Scholar
  24. 24.
    Papazoglou, Mike P. Service-Oriented Computing: Concepts, Characteristics and Directions. Proceedings of the Fourth International Conference on Web Information Systems Engineering (WISE’03). USA: IEEE Computer Society. (2003).Google Scholar
  25. 25.
    Park, Jin Woo, Park, Si Hyoung, Moon, Ji Joong, Yoon, Youngha and Kim, Seung Jo. High-Fidelity Simulation Based Optimum Design Utilizing Computing Grid Technology. 46th AIAA/ASME/ASCE//AHS/ASC Structures, Structural Dynamics & Materials Conference. USA: AIAA Inc. (2005).Google Scholar
  26. 26.
    Parmee, I. C., Abraham, J., Shackelford, M., Rana, O. F. and Shaikhali, A. Towards Autonomous Evolutionary Design Systems via Grid-Based Technologies. Proceedings of ASCE 2005 International Conference on Computing in Civil Engineering (IPDPS’03). ASCE. (2005).Google Scholar
  27. 27.
    Pattnaik, Pratap, Ekanadham, Kattamuri and Jann, Joefon. Autonomic Computing and Grid. Berman, Fran, Fox, Geofrey C. and Hey, Anthony J. G. Grid Computing: Making the Global Infrastructure a Reality. England, UK: Jonh Wiley & Sons Ltd. (2003) 351-384.Google Scholar
  28. 28.
    Pearl, Laura, Welch, Von, Foster, Ian, Carl, Kesselman and Tuecke, Steven. A Community Authorization Service for Group Collaboration. Proceedings of the Third International Workshop on Policies for Distributed Systems and Networks (POLICY’02). USA: IEEE Computer Society. (2002).Google Scholar
  29. 29.
    Pound, G.E., Eres, M.H., Wason, J.L., Jiao, Z., Keane, A.J. and Cox, S.J. A Grid-Enabled Problem Solving Environment (PSE) for Design Optimisation within Matlab. Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS’03). USA: IEEE Computer Society. (2003).Google Scholar
  30. 30.
    Robertson, C. and Fisher, R. B. Better Surface Interactions by Constrained Evolution. Parmee, I. C. Adaptive Computing in Design and Manufacture. 5th ed. London, UK: Springer-Verlag London Limited. (2002) 133-142.Google Scholar
  31. 31.
    Rohl, Peter J.; Kolonay, Raymond M.; Irani, Rohinton K.; Sobolewski, Michael; Kao, Kevin, and Bailey, Michael W. A Federated Intelligent Product Environment. AIAA. (2000).Google Scholar
  32. 32.
    Roure, David De; Jennings, Nicholas R., and Shadbolt, Nigel R. The Semantic Grid: A Future e-Science Infrastructure. Berman, Fran//Fox, Geofrey C.//Hey, Anthony J. G. Grid Computing: Making the Global Infrastructure a Reality. England, UK: John Wiley & Sons Ltd. (2003) 437-470.Google Scholar
  33. 33.
    Schikuta, Erich and Weishaupl, Thomas. N2Grid: Neural Networks in the Grid. IEEE. (2004) 1409-1414.Google Scholar
  34. 34.
    Scurr, A. D. and Keane, A. J. The Development of a Grid-Based Engineering Design Problem Solving Environment. Parmee, I. C. Adaptive Computing in Design and Manufacture. 5th ed. England, UK: Springer-Verlag London Limited. (2002) 65-73.Google Scholar
  35. 35.
    Sobolewski, Michael and Kolonay, Raymond M. Federated Grid Computing with Interactive Service-Oriented Programming. Concuurent Engineering: Research and Applications. (2006) 1455-66.Google Scholar
  36. 36.
    Song, Wenbin; Ong, Yew Soon; Ng, Hee Khiang; Keane, Andy; Cox, Simon, and Lee, Bu Sung. A Service-Oriented Approach for Aerodynamic Shape Optimisation Across Institutional Boudaries. 2004 8th International Conference on Control, Automation, Robotics and Vision. USA: IEEE. (2004).Google Scholar
  37. 37.
    Soorianayanan, Sekhar and Sobolewski, Michael. Monitoring Federated Services in CE Grids. Sobolewski, Michael//Cha, Jianzhong. Concurrent Engineering: The Worldwide Engineering Grid. China: Tsinghua University Press and Springer-Verlag. (2004) 89-95.Google Scholar
  38. 38.
    Wager, Tor D. and Nichols, Thomas E. Optimisation of Experimental Design in fMRI: A General Framework using a Genetic Algorithm. IEEE NeuroImage Academic Press. (2003) 18293-309.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ashutosh Tiwari
    • 1
  • Gokop Goteng
    • 1
  • Rajkumar Roy
    • 1
  1. 1.Decision Engineering Centre, Manufacturing Department, School of Applied ScCranfield UniversityBedfordshireUK

Personalised recommendations