Abstract
In a stationary optimization problem, the fitness landscape does not change during the optimization process; and the goal of an optimization algorithm is to locate a stationary optimum. On the other hand, most of the real world problems are dynamic, and stochastically change over time. Genetic Algorithms have been applied to dynamic problems, recently. In this study, we present two hybrid techniques that are applied on moving peaks benchmark problem, where these techniques are the extensions of the leading methods in the literature. Based on the experimental study, it was observed that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem.
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References
Bierwirth, C., Copher, H.: Dynamic task scheduling with genetic algorithms in manufacturing systems, Technical report, Department of Economics, University of Bremen, Germany (1994)
Branke, J.: Evolutionary Algorithms for dynamic optimization problems a survey, Technical Report 387, Institute AIFB, University of Kalsruhe (February 1999)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Congress on Evolutionary Computation, vol. 3, pp. 1875–1882. IEEE, Los Alamitos (1999)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)
Branke, J., Kauler, T., Schmidt, C., Schmeck, H.: Multi-population approach to dynamic optimization problems. In: Adaptive Computing in Design and Manufacture - ACDM 2000, pp. 299–308. Springer, Berlin (2000)
Branke, J., Salihoglu, E., Uyar, S.: Towards an analysis of dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference-GECCO 2005, pp. 1433–1440 (2005)
Gerfenstette, J.J.: Genetic algorithms for changing environments. In: Parallel Problem Solving From Nature 2, pp. 137–144. North Holland, Amsterdam (1992)
De Jong, K.: An analysis of the behavior of a class of genetic adaptive systems, PhD thesis, University of Michigan, Ann Arbor MI (1975)
Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation 12(3), 273–302 (2004)
Ursem, R.K.: Multinational GAs, Multimodal optimization techniques in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), Las Vegas, USA (2000)
Lin, S.C., Goodman, E.D., Punch, W.F.: A genetic algorithm approach to dynamic job shop scheduling problems. In: Seventh International Conference on Genetic Algorithms, pp. 481–488 (1997)
Cedeno, W., Vemuri, V.R.: On the use of niching for dynamic landscapes. In: Intl. Conf. on Evolutionary Computation, IEEE, Los Alamitos (1997)
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Ayvaz, D., Topcuoglu, H., Gurgen, F. (2006). Hybrid Techniques for Dynamic Optimization Problems. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_12
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DOI: https://doi.org/10.1007/11902140_12
Publisher Name: Springer, Berlin, Heidelberg
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