Abstract
Some of the earlier studies on dynamic environments focus on understanding the nature of the changes. However, very few of them use the information obtained to characterize the change for designing better solver algorithms. In this paper, a classification-based single point search algorithm, which makes use of the characterization information to react differently under different change characteristics, is introduced. The mechanisms it employs to react to the changes resemble hyper-heuristic approaches previously proposed for dynamic environments. Experiments are performed to understand the underlying components of the proposed method as well as to compare its performance with similar single point search-based hyper-heuristic approaches proposed for dynamic environments. The experimental results are promising and show the strength of the proposed heuristic approach.
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
Notes
- 1.
In this study, the settings for different severity classes are determined more widely sparsed compared to Kiraz’s study for capturing the behavior of the changing environment. Since the severity settings differ, the experimental results are also different.
References
Branke, J.: Evolutionary Algorithms for Dynamic Optimization Problems: A Survey. AIFB (1999)
Branke, J.: Evolutionary Optimization in Dynamic Environments (2001)
Branke, J., Salihoğlu, E., Uyar, Ş.: Towards an analysis of dynamic environments. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1433–1440. ACM (2005)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cobb, H.G.: An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments. Technical Report. Naval Research Lab Washington, DC (1990)
Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: International Conference on the Practice and Theory of Automated Timetabling, pp. 176–190. Springer (2000)
Cowling, P., Kendall, G., Soubeiga, E.: Hyperheuristics: a tool for rapid prototyping in scheduling and optimisation. In: Workshops on Applications of Evolutionary Computation, pp. 1–10. Springer (2002)
Cruz, C., González, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput. 15(7), 1427–1448 (2011)
De Jong, K.: Evolving in a changing world. In: International Symposium on Methodologies for Intelligent Systems, pp. 512–519. Springer (1999)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments—a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)
Karaman, A., Uyar, Ş., Eryiğit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Workshops on Applications of Evolutionary Computation, pp. 563–573. Springer (2005)
Kiraz, B., Etaner-Uyar, A.Ş., Özcan, E.: Selection hyper-heuristics in dynamic environments. J. Oper. Res. Soc. 64(12), 1753–1769 (2013)
Morrison, R.W.: A new EA for dynamic problems. In: Designing Evolutionary Algorithms for Dynamic Environments, pp. 53–68. Springer (2004)
Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Peng, X., Gao, X., Yang, S.: Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Comput. 15(2), 311–326 (2011)
Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 3–28. Springer (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yıldırım-Bilgi, Ş., Etaner-Uyar, A.Ş. (2021). A Classification-Based Heuristic Approach for Dynamic Environments. In: Matoušek, R., Kůdela, J. (eds) Recent Advances in Soft Computing and Cybernetics. Studies in Fuzziness and Soft Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-030-61659-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-61659-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61658-8
Online ISBN: 978-3-030-61659-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)