Abstract
We present a new nature-inspired algorithm, mt–GA, which is a parallelized version of a simple GA, where subpopulations evolve independently from each other and on different threads. The overall goal is to develop a population-based algorithm capable to escape from local optima. In doing so, we used complex trap functions, and we provide experimental answers to some crucial implementation decision problems. The obtained results show the robustness and efficiency of the proposed algorithm, even when compared to well-known state-of-the art optimization algorithms based on the clonal selection principle.
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
Cantú-Paz, E.: Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms. J. Heuristics 7(4), 311–334 (2001)
Cheng, H., Yang, S.: Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 562–571. Springer, Heidelberg (2010)
Cutello, V., Nicosia, G., Pavone, M.: Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)
Cutello, V., Narzisi, G., Nicosia, G., Pavone, M., Sorace, G.: How to Escape Traps Using Clonal Selection Algorithms. In: 1st International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 1, pp. 322–326. INSTICC Press (2004)
Cutello, V., Narzisi, G., Nicosia, G., Pavone, M.: Clonal Selection Algorithms: A Comparative Case Study using Effective Mutation Potentials. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 13–28. Springer, Heidelberg (2005)
De Castro, L.N., Von Zuben, F.J.: Learning and Optimization using the Clonal Selection Principle. IEEE Transaction on Evolutionary Computation 6(3), 239–251 (2002)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144 (1992)
Nijssen, S., Back, T.: An analysis of the behavior of semplified evolutionary algorithms on trap functions. IEEE Transaction on Evolutionary Computation 7(1), 11–22 (2003)
Pavone, M., Narzisi, G., Nicosia, G.: Clonal Selection - An Immunological Algorithm for Global Optimization over Continuous Spaces. Journal of Global Optimization 53(4), 769–808 (2012)
Prugel-Bennett, A., Rogers, A.: Modelling GA Dynamics. In: Theoretical Aspects of Evolutionary Computing, pp. 59–86 (2001)
Yang, S.: Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments. Evolutionary Computation 16(3), 385–416 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cutello, V., De Michele, A.G., Pavone, M. (2014). Escaping Local Optima via Parallelization and Migration. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-01692-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01691-7
Online ISBN: 978-3-319-01692-4
eBook Packages: EngineeringEngineering (R0)