Modeling and simulation of genetic algorithm using meta-models

Document Type : Research Paper

Author

Faculty of Electrical Engineering, Sahand University of Technology, Sahand, Iran

Abstract

In this paper we introduce new method for modeling and simulation of genetic algorithm based on meta-models. Eclipse Modeling Framework (EMF) provides a meta-modeling framework to describe models in various abstraction levels. We use this platform and EMF-DEVS to modeling genetic algorithm (GA-DEVS) and generating partial executable codes on DEVS-Suite simulator. Also this process helps modelers to validate models and their structure and I/O validity.

Keywords


[1] Zeigler, B.P., H. Praehofer, and T.G. Kim. Theory of modeling and simulation.Academic press, New York, second edition, (2000).
[2] Steinberg, D., F. Budinsky, M. Paternostro, and E. Merks. EMF: Eclipse Modeling Framework. Second ed, Addison-Wesley, (2008).
[3] Cetinkaya, D., A. Verbraeck, and M. D. Seck. Model Transformation from BPMN to DEVS in a Prototype Implementation of the MDD4MS Framework, In DEVS Symposium, Spring Simulation Multi-Conference, March, Orlando, FL, (2012).
[4] Achilleos, A., N. Georgalas, and K. Yang. An Open Source Domain-Specific Tools Framework to Support Model Driven Development of OSS. In Proceedings of the 3rd European Conference on Model Driven Architecture - Foundations and Appli-cations (ECMDA-FA 2007).
[5] Ohenoja, Markku, and Kauko Leivisk. Validation of genetic algorithm results in a fuel cell model international journal of hydrogen energy 35.22 (2010): 12618-12625.
[6] Warmer, J. and A. Kleppe. Object Constraint Language, The Getting Your Models Ready for MDA, Second Edition. Addison Wesley, 245 pages, (2003).
[7] Yousefi, Milad, et al. Chaotic genetic algorithm and Adaboost ensemble metamod-eling approach for optimum resource planning in emergency departments, Artificial Intelligence in Medicine (2017).
[8] Zeigler, B. P., and H. S. Sarjoughian. Guide to Modeling and Simulation of Systems of Systems, Springer, (2013).
[9] Daz-Manrquez, A., Toscano, G., & Coello, C. A. C. Comparison of metamodeling techniques in evolutionary algorithms, Soft Computing, 21(19), 5647-5663, (2017)..
[10] Roy, Proteek, Rayan Hussein, and Kalyanmoy Deb. Metamodeling for multimodal selection functions in evolutionary multi-objective optimization, Proceedings of the Genetic and Evolutionary Computation Conference. ACM, (2017).
[11] Sarjoughian, H. S., and A. Mahmoodi Markid. EMF-DEVS Modeling. In DEVS Symposium, Spring Simulation Multi-Conference, March, Orlando, FL, (2012).
[12] Mahmoodi, A., DEVS-MDA a new architecture for DEVS simulations, 10th Na-tional Conference on Computer and Intelligent Systems, Tabriz, Iran, (2013).
[13] Sarjoughian, H.S. DEVS-Suite Simulator. http://devs-suitesim.sf.net , (2009).
[14] Mittal, S., and S. A. Douglass. DEVSML 2.0: The Language and the Stack, Spring Simulation Multiconference, Orlando, FL, March (2012).
[15] Lazar, I., B. Parv, S. Motogna, I.-G. Czibula, and C.-L. Lazar. An Agile MDA Approach for Executable UML Structured Activities, Studia Univ. Babes-Bolyai, vol. LII, no. 2, pp. 101-114, (2007).
[16] Sarjoughian, H.S., and V. Elamvazhuthi. CoSMoS: a visual environment for component-based modeling, experimental design, and simulation, In Proceedings of the International Conference on Simulation Tools and Techniques, SIMUTools,
pages 19, Rome, Italy, (2009).
[17] Mahmoodi Markid, A. New Method for Design and Development of Highly Scalable Networks using Meta-Models, In Proceedings of the 13th National Conference on Computer and Intelligent Systems, TSPI13, Tabriz, Iran, (2016).