COORDINATING EVOLUTION - Designing a Self-adapting Distributed Genetic Algorithm

Nikolaos Chatzinikolaou

Abstract

In large scale optimisation problems, the aim is to find near-optimal solutions in very large combinatorial spaces. This learning/optimisation process can be aided by parallelisation, but it normally is difficult for engineers to decide in advance how to split the task into appropriate segments attuned to the agents working on them. This paper chooses a particular style of algorithm (a form of genetic algorithm) and describes a framework in which the parallelisation and tuning of the multi-agent system is performed automatically using a combination of self-adaptation of the agents plus sharing of negotiation protocols between agents. These GA agents are optimised themselves through the use of an evolutionary process of selection and recombination. Agents are selected according to the fitness of their respective populations, and during the recombination phase they exchange individuals from their population as well as their optimisation parameters, which is what lends the system its self-adaptive properties. This allows the execution of optimal optimisations without the burden of tuning the evolutionary process by hand. The architecture we use has been shown to be capable of operating in peer to peer environments, raising confidence in its scalability through the autonomy of its components.

References

  1. Ackley, D. H. (1987). A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell, MA, USA.
  2. Alba, E. and Troya, J. M. (1999). A survey of parallel distributed genetic algorithms. Complexity, 4(4):31-52.
  3. Arenas, M. G., Collet, P., Eiben, A. E., Jelasity, M., Guervós, J. J. M., Paechter, B., Preuß, M., and Schoenauer, M. (2002). A framework for distributed evolutionary algorithms. In PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, pages 665-675, London, UK. Springer-Verlag.
  4. Back, T. (1992). Self-adaptation in genetic algorithms. In Proceedings of the First European Conference on Artificial Life, pages 263-271. MIT Press.
  5. Belding, T. C. (1995). The distributed genetic algorithm revisited. In Proceedings of the 6th International Conference on Genetic Algorithms, pages 114-121, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  6. Cant-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Paralleles, 102.
  7. Clune, J., Goings, S., Punch, B., and Goodman, E. (2005). Investigations in meta-gas: panaceas or pipe dreams? In GECCO 7805: Proceedings of the 2005 workshops on Genetic and evolutionary computation, pages 235- 241, New York, NY, USA. ACM.
  8. Eiben, A. E., Hinterding, R., Hinterding, A. E. E. R., and Michalewicz, Z. (2000). Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3:124-141.
  9. Foster, I., Zhao, Y., Raicu, I., and Lu, S. (2008). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE 7808, pages 1-10.
  10. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.
  11. Grefenstette, J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 16(1):122-128.
  12. Hesser, J. and Männer, R. (1991). Towards an optimal mutation probability for genetic algorithms. In PPSN I: Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, pages 23-32, London, UK. Springer-Verlag.
  13. Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.
  14. Kisiel-Dorohinicki, M., Socha, K., and Communication, S. T. E. (2001). Crowding factor in evolutionary multiagent system for multiobjective optimization. In Proceedings of IC-AI01 International Conference on Artificial Inteligence. CSREA Press.
  15. Lim, D., Ong, Y.-S., Jin, Y., Sendhoff, B., and Lee, B.-S. (2007). Efficient hierarchical parallel genetic algorithms using grid computing. Future Gener. Comput. Syst., 23(4):658-670.
  16. Meyer-Nieberg, S. and Beyer, H.-G. (2006). Self-adaptation in evolutionary algorithms. In Parameter Setting in Evolutionary Algorithm, pages 47-76. Springer.
  17. Michalewicz, Z. (1996). Genetic algorithms + data structures = evolution programs (3rd ed.). Springer-Verlag, London, UK.
  18. Munawar, A., Wahib, M., Munetomo, M., and Akama, K. (2008). A survey: Genetic algorithms and the fast evolving world of parallel computing. High Performance Computing and Communications, 10th IEEE International Conference, pages 897-902.
  19. Nowostawski, M. and Poli, R. (1999). Parallel genetic algorithm taxonomy. In Proceedings of the Third International, pages 88-92. IEEE.
  20. Robertson, D. (2004a). A lightweight coordination calculus for agent systems. In In Declarative Agent Languages and Technologies, pages 183-197.
  21. Robertson, D. (2004b). Multi-agent coordination as distributed logic programming. In International Conference on Logic Programming, Sant-Malo, France.
  22. Robertson, D., Giunchiglia, F., van Harmelen, F., Marchese, M., Sabou, M., Schorlemmer, M., Shadbolt, N., Siebes, R., Sierra, C., Walton, C., Dasmahapatra, S., Dupplaw, D., Lewis, P., Yatskevich, M., Kotoulas, S., de Pinninck, A. P., and Loizou, A. (2006). Open knowledge semantic webs through peer-to-peer interaction. Technical Report DIT-06-034, University of Trento.
  23. Ross, P. and Corne, D. (1995). Applications of genetic algorithms. In On Transcomputer Based Parallel Processing Systems, Lecture.
  24. Schwefel, H.-P. (1981). Numerical Optimization of Computer Models. John Wiley & Sons, Inc., New York, NY, USA.
  25. Socha, K. and Kisiel-Dorohinicki, M. (2002). Agentbased evolutionary multiobjective optimisation. In in Proceedings of the Fourth Congress on Evolutionary Computation, pages 109-114. press.
  26. Tanese, R. (1989). Distributed genetic algorithms. In Proceedings of the third international conference on Genetic algorithms, pages 434-439, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  27. Tuson, A. L. (1995). Adapting operator probabilities in genetic algorithms. Technical report, Master's thesis, Evolutionary Computation Group, Dept. of Artificial Intelligence, Edinburgh University.
  28. Yoshihiro, E. T., Murata, Y., Shibata, N., and Ito, M. (2003). Self adaptive island ga. In 2003 Congress on Evolutionary Computation, pages 1072-1079.
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in Harvard Style

Chatzinikolaou N. (2010). COORDINATING EVOLUTION - Designing a Self-adapting Distributed Genetic Algorithm . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 13-20. DOI: 10.5220/0002871200130020


in Bibtex Style

@conference{iceis10,
author={Nikolaos Chatzinikolaou},
title={COORDINATING EVOLUTION - Designing a Self-adapting Distributed Genetic Algorithm},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={13-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002871200130020},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - COORDINATING EVOLUTION - Designing a Self-adapting Distributed Genetic Algorithm
SN - 978-989-8425-05-8
AU - Chatzinikolaou N.
PY - 2010
SP - 13
EP - 20
DO - 10.5220/0002871200130020