Abstract
The topological features of the communication network between computing nodes in Parallel Genetic Algorithms, under the framework of the island model, is discussed in the context of both the local rate of information exchange between nodes, and the global exchange rate that measures the level of information flow in the entire network. For optimal performance of parallel genetic algorithm for a set of benchmark functions, the connectivity of the network can be found, corresponding to a global information exchange rate between 40-70%. This range is obtained by statistical analysis on the search for solutions of four benchmark problems: the 0-1 knapsack, the Weierstrass’s function, the Ackley’s function, and the Modified Shekel’s foxholes function. Our method is based on the cutting of links of a fully connected network to gradually decrease the connectivity, and compare the performance of the genetic algorithm on each network. Suggestions for the protocol in applying this general guideline in the design of a good communication network for parallel genetic algorithms are made, where the islands are connected with 40% of links of a fully connected network before fine tuning the parameters of the island model to enhance performance in a specific problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Li, S.P., Szeto, K.Y.: Crytoarithmetic problem using parallel Genetic Algorithms. In: Mendl 1999 (1999)
Szeto, K.Y., Cheung, K.H.: Multiple time series prediction using genetic algorithms optimizer. In: IDEAL 1998, pp. 127–133 (1998)
Jiang, R., Szeto, K.Y., Luo, Y.P., Hu, D.C.: Distributed parallel genetic algorithm with path splitting scheme for the large traveling salesman problems. In: WCC 2000, pp. 478–485 (2000)
Szeto, K.Y., Cheung, K.H., Li, S.P.: Effects of dimensionality on parallel genetic algorithms. In: Proceedings of the 4th ISAS Conference, vol. 2, pp. 322–325 (1998)
Fong, A.L.Y., Szeto, K.Y.: Rule Extraction in Short Memory Time Series using Genetic Algorithms. European Physical Journal B 20, 569–572 (2001)
Shiu, K.L., Szeto, K.Y.: Self-adaptive Mutation Only Genetic Algorithm: An Application on the Optimization of Airport Capacity Utilization. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 428–435. Springer, Heidelberg (2008)
Bondi, A.B.: Characteristics of scalability and their impact on performance. In: Proceedings of the 2nd International Workshop on Software and Performance, pp. 195–203 (2000)
Cantú-Paz, E.: Efficient and accurate parallel genetic algorithms, pp. 16, 17, 22. Kluwer Academic, USA (2000)
Gustafson, S., Burke, E.K.: The Speciating Island Model: An alternative parallel evolutionary algorithm. Journal of Parallel and Distributed Computing 66, 1025–1036 (2006)
Scheutz, M., Schermerhorn, P.: Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models. Journal of Parallel and Distributed Computing 66, 1037–1051 (2006)
Melab, N., Cahon, S., Talbi, E.-G.: Grid computing for parallel bio-inspired algorithms. Journal of Parallel and Distributed Computing 66, 1052–1061 (2006)
Delaossa, L., Gámez, J.A., Puerta, J.M.: Initial approaches to the application of islands-based parallel EDAs in continuous domains. Journal of Parallel and Distributed Computing 66, 991–1001 (2006)
Wang, C.G., Szeto, K.Y.: Sales Potential Optimization on Directed Social Networks: A Quasi-Parallel Genetic Algorithm Approach. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 114–123. Springer, Heidelberg (2012)
Wang, G., Wu, D., Chen, W., Szeto, K.Y.: Importance of Information Exchange in Quasi-Parallel Genetic Algorithms. In: GECCO 2011, pp. 127–128 (2011)
Szeto, K.Y., Zhang, J.: Adaptive Genetic Algorithm and Quasi-parallel Genetic Algorithm: Application to Knapsack Problem. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2005. LNCS, vol. 3743, pp. 189–196. Springer, Heidelberg (2006)
Ma, C.W., Szeto, K.Y.: Locus Oriented Adaptive Genetic Algorithm: Application to the Zero/One Knapsack Problem. In: RASC 2004, pp. 410–415 (2004)
Law, N.L., Szeto, K.Y.: Adaptive Genetic Algorithm with Mutation and Crossover Matrices. In: IJCAI 2007, pp. 2330–2333 (2007)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization (2005)
Ackley, D.H.: A connectionist machine for genetic hill climbing. Kluwer Academic Publishers, Boston (1987)
Bersini, H., Dorigo, M., Langerman, S., Seront, G., Gambardella, L.: Results of the first international contest on evolutionary optimisation (1st iceo), CEC 1996, pp. 611–615 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guan, W., Szeto, K.Y. (2013). Topological Effects on the Performance of Island Model of Parallel Genetic Algorithm. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-38682-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38681-7
Online ISBN: 978-3-642-38682-4
eBook Packages: Computer ScienceComputer Science (R0)