Abstract
It is possible to solve slot machine RTP optimization problem by using evolutionary algorithms. In practice this optimization is done by hand adjustment of the symbols placed on the game reels. By arranging symbols positions, it is possible to achieve optimal return to player percentage (RTP). Equalization of the prizes distribution, generated by different win combinations, can be optimized also. In this paper a DE based RTP optimization and prizes equalization is proposed. DE is used in its discrete variation, because the problem of optimal symbols distribution on the reels is in the discrete domain. DE is selected as an alternative to genetic algorithms (GA) because of its faster convergence. The convergence is a key factor in such optimizations, because each fitness value is calculated based on intensive Monte-Carlo simulations. The scope of this paper is focused on the symbols distribution placed on the machine reels in such a way that two common goals to be satisfied - desired RTP and keeping relatively equal levels of the prizes (prizes expressed as amount of money won from combinations with each particular symbol), with relatively good symbol diversity on the reels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Inge, S.: Electronic gaming device utilizing a random number generator for selecting the reel stop positions, US 4448419 A (1984). Published on 15 May 1984
Cooper, M.: How slot machines give gamblers the business, The Atlantic Monthly Group (2005). Accessed on 21 April 2008
Casino Observer: How to play slots. CasinoObserver.com (2013). http://casinoobserver.com/how-to-play-slots.htm, Accessed on 06 March 2013
Balabanov, T.: Bingo slot discrete differential evolution optimization of RTP and prizes distribution, (2015). http://github.com/TodorBalabanov/BingoSlotDifferentialEvolutionOptimization/
Storn, R.: Differential evolution - a simple and efficient heuristic strategy for global optimization over continuous spaces, J. Global Optim., 11, 341–359. Dordrecht (1997)
Price, K.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. Mc Graw-Hill, UK (1999)
Goldberg, D.: Genetic algorithms in search, Optimization and Machine Learning, Reading. Addison-Wesly Publishing Co., Massachusetts (1989)
Schwefel, H.-P. (ed.): Evolution and Optimization Seeking. John Wiley and Sons, New York (1995)
Mezura-Montes, E., Velazquez-Reyes, J., Coello, C.: Modified differential evolution for constrained optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 25–32. Vancouver (2006)
Ivanov, V.: On the approach for automatic generation of small embedded PicoBlaze system. In: Proceedings of the 13th International Conference on ACSD, pp. 257–260. Barcelona (2013)
Balabanov, T., Zankinski, I., Shumanov, B.: Slot machines RTP optimization with genetic algorithms. In: Dimov, I., Fidanova, S., Lirkov, I. (eds.) NMA 2014. LNCS, vol. 8962, pp. 55–61. Springer, Heidelberg (2015)
Acknowledgements
This work was supported by the Bulgarian National Scientific Fund under the grants DFNI 02/20 Efficient Parallel Algorithms for Large Scale Computational Problems and DFNI 02/5 InterCriteria Analysis A New Approach to Decision Making.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Balabanov, T., Zankinski, I., Shumanov, B. (2015). Slot Machine RTP Optimization and Symbols Wins Equalization with Discrete Differential Evolution. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-26520-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26519-3
Online ISBN: 978-3-319-26520-9
eBook Packages: Computer ScienceComputer Science (R0)