Conformance Evaluation of Genetic Algorithm for Evolutionary Area Search of Canonical Model
- 6 Downloads
The theory and practice of genetic algorithms is largely based on the Schema Theorem. It was formulated for a canonical genetic algorithm and proves its ability to generate a sufficient number of effective schemata of individuals. Genetic algorithms to solve specific problems and to be different from canonical ones have to be checked to find out whether the Schema Theorem evaluates the algorithm fitness. The article validates the way of testing the algorithm developed as a technique of an area search. The methodology and research results are stated consistently. Coding specifics of the search queries are noted, a criterion of the coding method applicability is substantiated. A variant of the genotype geometric coding is proposed. In comparison with other methods of binary search coding, it provides a short code length and uniqueness as well as conforms the formulated criterion of applicability. Supporting experimental results are given. The Schema Theorem is shown to hold with the iterative execution of the genetic algorithm being tested.
Keywords and phrasesgenetic algorithm genotype query coding crossover defining length order scheme subject search Holland’s schema theorem fitness function
Unable to display preview. Download preview PDF.
The authors express their thanks the colleagues who have been taking part in this study, namely, P. I. Meskin and N. V. Vinogradova as well as to translator of the article O. A. Gumenyuk.
This work was done at the Tver State Technical University with supporting of the Russian Foundation of Basic Research (project no. 18-07-00358) and at the Joint Supercomputer Center of the Russian Academy of Sciences Branch of NIISI RAS within the framework of the State assignment (research topic 065-2019-0014).
- 5.B. Rafael, M. Affenzeller, and S. Wagner, in Proceedings of the GECCO'12 Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012, pp. 469–476.Google Scholar
- 6.A. B. Glushak, V. A. Lomazov, and D. A. Petrosov, “Analysis of modified genetic algorithm based on the theory of schemes,” Nauch. Vedom. BelGU, Mat. Fiz. 27 (248), 121–126 (2016).Google Scholar
- 8.L. C. M. de Paula, A. S. Soares, T. W. de Lima, A. R. G. Filho, and C. J. Coelho, in Proceedings of the GECCO'16 Companion of the 2016 onGenetic and Evolutionary Computation Conference Companion, 2016, pp. 1031–1034.Google Scholar
- 9.O. A. Melikhova, “Genetic algorithms application for the artificial intellect systems construction,” Izv. SFedU, Inzhen. Nauki 7 (144), 53–58 (2013).Google Scholar
- 10.S. Noor, M. I. Lali, and M. S. Nawaz, “Solving job shop scheduling problem with genetic algorithm,” Sci. Int. 27, 3367–3371 (2015).Google Scholar
- 11.S. L. Keast, “A simple representation technique to improve GA performance,” Aauburn Univ. Tech. Rep. CSSE03–11 (2003), pp. 1–21. ftp://ftp.eng.auburn.edu/pub/techreports/csse/03/CSSE03-11.pdf. Accessed 2019.Google Scholar
- 12.C. R. Cox and R. A. Watson, in Proceedings of the GECCO'14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014, pp. 341–348.Google Scholar
- 13.L. Sun, X. Cheng, and Y. Liang, “Solving job shop scheduling problem using genetic algorithm with penalty function,” Int. J. Intell. Inform. Process. 1 (2), 65–77 (2010).Google Scholar
- 14.Chen Lin, in Proceedings of the 4th International Conference on Genetic and Evolutionary Computing, 2010, pp. 301–304.Google Scholar
- 15.A. A. Kazharov and V.M. Kureichik, “Template using for ant colony algorithms,” Izv. SFedU, Inzhen. Nauki 7 (144), 17–22 (2013).Google Scholar
- 16.B. Burlacu, M. Kommenda, and M. Affenzeller, in Proceedings of the Asia-Pacific Conference on Computer Aided System Engineering, 2015, pp. 152–157.Google Scholar
- 17.D. Rutkowska, M. Pilinski, and L. Rutkowski, Neural Networks, Genetic Algorithms and Fuzzy Systems (PWN, Warsaw, 1997; Hotline-Telekom, Moscow, 2013).Google Scholar