Abstract
In this work, the relation between number of ES iterations and convergence of the whole GPA-ES hybrid algorithm will be studied due to increasing needs to analyze and model large data sets. Evolutionary algorithms are applicable in the areas which are not covered by neural networks and deep learning like search of algebraic model of data. The difference between time and algorithmic complexity will be also mentioned as well as the problems of multitasking implementation of GPA, where external influences complicate increasing of GPA efficiency via Pseudo Random Number Generator (PRNG) choice optimization.
Hybrid evolutionary algorithms like GPA-ES uses GPA for solution structure development and Evolutionary Strategy (ES) for parameters identification are controlled by many parameters. The most significant are sizes of GPA population and sizes of ES populations related to each particular individual in GPA population. There is also limit of ES algorithm evolutionary cycles. This limit plays two contradictory roles. On one side bigger number of ES iterations means less chance to omit good solution for wrongly identified parameters, on the opposite side large number of ES iterations significantly increases computational time and thus limits application domain of GPA-ES algorithm.
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References
Brandejsky, T.: Evolutionary system to model structure and parameters regression. Neural Netw. World 12(2), 181–194 (2012). ISSN 1210-0552
Brandejsky, T.: The use of local models optimized by genetic programming algorithm in biomedical-signal analysis. In: Zelinka, I., Snasel, V., Abraham, A. (eds.) Handbook of Optimization from Classical to Modern Approach, pp. 697–716 (2012). ISSN 1868-4394, ISBN 978-3-642-30503-0
Alander, T.: On optimal population size of genetic algorithms. In: Proceedings of the IEEE Computer Systems and Software Engineering, pp. 65–69 (1992)
Eiben, A.E., Hinterding, R., Michalewic, Z.: Parameter control in evolutionary algorithms. Trans. Evol. Comput. 3(2), 124–141 (1999). https://doi.org/10.1109/4235.771166
Koumousis, K., Katsaras, C.P.: A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006). https://doi.org/10.1109/TEVC.2005.860765
Lobo, G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54. Springer, Heidelberg (2007). ISBN 978-3-540-69431-1
Reeves, C.R.: Using genetic algorithms with small populations. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, pp. 92–99 (1993). ISBN 1-55860-299-2
Piszcz, A., Soul, T.: Genetic programming: optimal population sizes for varying complexity problems. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO, Seattle, pp. 953–954 (2006). https://doi.org/10.1145/1143997.1144166
Brandejsky, T.: Small populations in GPA-ES algorithm. In: Matousek, R., (ed.) 19th International Conference on Soft Computing, MENDEL 2013, Brno, pp. 31–36 (2013). ISSN 1803-3814, ISBN 978-80-214-4755-4
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Brandejsky, T. (2020). Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_29
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