Abstract
In this paper, we describe and test a new evolutionary algorithm based on the notion of a schema, which is designed to solve global optimization problems. We call it Free Dynamic Schema (FDS). It is a more refined variant of our previous DSC, DSDSC and MDSDSC algorithms. FDS processes two populations which are partially composed of the same chromosomes. The algorithm divides each population into several groups to which various genetic operators are applied: free dynamic schema, dissimilarity, similarity, and dynamic dissimilarity. Also, some new chromosomes are regenerated randomly. The FDS algorithm is applied to 22 test functions in 2, 4 and 10 dimensions. It is also compared with the classical GA, CMA-ES and DE algorithms. Moreover, the FDS algorithm is compared with the BA and PSA algorithms for some functions. In most cases, we have found the FDS algorithm to be superior to the classical GA and BA.
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References
Wu, Y., et al.: Dynamic self-adaptive double population particle swarm optimization algorithm based on Lorenz equation. J. Comput. Commun. 5(13), 9–20 (2017)
Park, T., Ryu, K.R.: A dual-population genetic algorithm for adaptive diversity control. IEEE Trans. Evol. Comput. 14(6), 865–884 (2010)
Mei, J., Wu, H.: Double bee population evolutionary genetic algorithm with filtering operation. In: Wen, Z., Li, T. (eds.) Foundations of Intelligent Systems. AISC, vol. 277, pp. 693–700. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54924-3_65
Al-Jawadi, R., Studniarski, M.: An optimization algorithm based on multi-dynamic schema of chromosomes. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018, Part I. LNCS (LNAI), vol. 10841, pp. 279–289. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91253-0_27
Al-Jawadi, R.: An optimization algorithm based on dynamic schema with dissimilarities and similarities of chromosomes. Int. J. Comput. Electr. Autom. Control Inf. Eng. 7(8), 1278–1285 (2016)
Al-Jawadi, R., Studniarski, M., Younus, A.: New genetic algorithm based on dissimilaries and similarities. Comput. Sci. J. AGH Univ. Sci. Technol. Pol. 19(1), 19 (2018)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996). https://doi.org/10.1007/978-3-662-03315-9
Sultan, A.B.M., Mahmod, R., Sukaiman, M.N., Abu Bakar, M.R.: Maintaining diversity for genetic algorithm: a case of timetabling problem. J. Teknol. Malaysia 44(D), 123–130 (2006)
CMA-ES. Matlab program. https://www.mathworks.com/matlabcentral/fileexchange/52898-cma-es-in-matlab
Price, K.V.: Differential Evolution, pp. 187–214 (2013). http://www.dii.unipd.it/~alotto/didattica/corsi/Elettrotecnica%20computazionale/DE.pdf
Eesa, A.S., Brifcani, A.M.A., Orman, Z.: A new tool for global optimization problems- cuttlefish algorithm. Int. J. Comput. Electr. Autom. Control Inf. Eng. 8(9), 1198–1202 (2014)
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The first author wishes to thank the Ministry of Higher Education and Scientific Research (MOHESR), Iraq.
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Al-Jawadi, R.Y., Studniarski, M., Younus, A.A. (2019). New Optimization Algorithm Based on Free Dynamic Schema. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_45
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