Abstract
In this paper we present an agent-based, peer-to-peer genetic algorithm capable of self-adaptation. We describe a preliminary architecture to that end, in which each agent is executing a local copy of a GA, using initially random parameters (currently restricted to the mutation rate for the purposes of experimentation). These GA agents are optimised themselves through the use of an evolutionary process of selection and recombination. Agents are selected according to the tness 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. Thanks to its parameter-less operation, our platform becomes more accessible and appealing to people outside the evolutionary computation community, and therefore a valuable tool in the eld of enterprise information systems. Initial empirical evaluation of the peer to peer architecture demonstrates better harnessing of the available resources, as well as added robustness and improved scalability.
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References
Ackley, D.H.: A connectionist machine for genetic hillclimbing. Kluwer Academic Publishers, Norwell (1987)
Alba, E., Troya, J.M.: A survey of parallel distributed genetic algorithms. Complexity 4(4), 31–52 (1999)
Arenas, M.G., Collet, P., Eiben, A.E., Jelasity, M., Merelo Guerv\’{o}s, J.J., Paechter, B., Preuß, M., Schoenauer, M.: A framework for distributed evolutionary algorithms. In: Guerv\’{o}s, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 665–675. Springer, Heidelberg (2002)
Back, T.: Self-adaptation in genetic algorithms. In: Proceedings of the First European Conference on Artificial Life, pp. 263–271. MIT Press, Cambridge (1992)
Belding, T.C.: The distributed genetic algorithm revisited. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 114–121. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Cant-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles 102 (1998)
Clune, J., Goings, S., Punch, B., Goodman, E.: Investigations in meta-gas: panaceas or pipe dreams? In: GECCO 2005: Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, pp. 235–241. ACM, New York (2005)
Eiben, A.E., Hinterding, R., Hinterding, A.E.E.R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (2000)
Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10 (2008)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 16(1), 122–128 (1986)
Hesser, J., Männer, R.: Towards an optimal mutation probability for genetic algorithms. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 23–32. Springer, Heidelberg (1991)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kisiel-Dorohinicki, M., Socha, K., Service Telematique Et Communication: Crowding factor in evolutionary multi-agent system for multiobjective optimization. In: Proceedings of IC-AI 2001: International Conference on Artificial Inteligence. CSREA Press (2001)
Lim, D., Ong, Y.-S., Jin, Y., Sendhoff, B., Lee, B.-S.: Efficient hierarchical parallel genetic algorithms using grid computing. Future Gener. Comput. Syst. 23(4), 658–670 (2007)
Meyer-Nieberg, S., Beyer, H.-G.: Self-adaptation in evolutionary algorithms. In: Parameter Setting in Evolutionary Algorithm, pp. 47–76. Springer, Heidelberg (2006)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, London (1996)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: A survey: Genetic algorithms and the fast evolving world of parallel computing. In: 10th IEEE International Conference on High Performance Computing and Communications, pp. 897–902 (2008)
Nowostawski, M., Poli, R.: Parallel genetic algorithm taxonomy. In: Proceedings of the Third International, pp. 88–92. IEEE, Los Alamitos (1999)
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., Loizou, A.: Open knowledge semantic webs through peer-to-peer interaction. Technical Report DIT-06-034, University of Trento (2006)
Robertson, D.: International Conference on Logic Programming, Sant-Malo, France (2004)
Robertson, D.: A lightweight coordination calculus for agent systems. In: Declarative Agent Languages and Technologies, pp. 183–197 (2004)
Ross, P., Corne, D.: Applications of genetic algorithms. In: On Transcomputer Based Parallel Processing Systems, Lecture (1995)
Schwefel, H.-P.: Numerical Optimization of Computer Models. John Wiley & Sons, Inc., New York (1981)
Socha, K., Kisiel-Dorohinicki, M.: Agent-based evolutionary multiobjective optimisation. In: Proceedings of the Fourth Congress on Evolutionary Computation, pp. 109–114. Press (2002)
Tanese, R.: Distributed genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 434–439. Morgan Kaufmann Publishers Inc., San Francisco (1989)
Tuson, A.L.: Adapting operator probabilities in genetic algorithms. Technical report, Master’s thesis, Evolutionary Computation Group, Dept. of Artificial Intelligence, Edinburgh University (1995)
Yoshihiro, E.T., Murata, Y., Shibata, N., Ito, M.: Self adaptive island ga. In: 2003 Congress on Evolutionary Computation, pp. 1072–1079 (2003)
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Chatzinikolaou, N. (2011). Coordinating Evolution: An Open, Peer-to-Peer Architecture for a Self-adapting Genetic Algorithm. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2010. Lecture Notes in Business Information Processing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19802-1_12
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DOI: https://doi.org/10.1007/978-3-642-19802-1_12
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