Slot Machine Base Game Evolutionary RTP Optimization

  • Delyan KeremedchievEmail author
  • Petar Tomov
  • Maria Barova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10187)


Slot machines are casino gambling machines with three or more reels which spin when a button is pushed. The machine pays are based on patterns of symbols visible on the front of the machine when it stops. Most of the modern slots consist of a base game, free games and bonus games. The base game is the core of the playing process. Player’s money are usually taken as bet in the base game and no bet is taken during free games or bonus games. Each slot machine has a parameter called return to player (RTP). RTP is the average amount of money which a player will get back, in average, after each spin of the reels. The total RTP (measured in percents) can be in the range between 75% and 98%. Its components are: base game RTP, free games RTP, bonus games RTP. The base game also controls how often free games will be activated and how often bonus games will be played. In this paper an evolutionary optimization algorithm for optimization of slot machine base game RTP by rearrangement of the symbols in the reels, is proposed. The problem itself is a combinatorial problem and the fitness function used checks all possible slot machine winning screens.


Slot machine Gambling Genetic algorithms Return to player Optimization 



This work was supported by private funding of Velbazhd Software LLC.


  1. 1.
    Inge, S.: Electronic gaming device utilizing a random number generator for selecting the reel stop positions. US 4448419 A, Published 1984–05-15 (1984)Google Scholar
  2. 2.
    Cooper, M.: How slot machines give gamblers the business. The Atlantic Monthly Group (2005). Accessed 21 Apr 2008Google Scholar
  3. 3.
    Observer, C.: How to Play Slots. (2013). Accessed 06 Mar 2013
  4. 4.
    Balabanov, T.: Slot machine base game evolutionary RTP optimization as parallel implementation with MPI (2016).
  5. 5.
    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, Cham (2015). doi: 10.1007/978-3-319-15585-2_6 Google Scholar
  6. 6.
    Balabanov, T., Zankinski, I., Shumanov, B.: Slot machine RTP optimization and symbols wins equalization with discrete differential evolution. In: Lirkov, I., Margenov, S.D., Waśniewski, J. (eds.) LSSC 2015. LNCS, vol. 9374, pp. 210–217. Springer, Cham (2015). doi: 10.1007/978-3-319-26520-9_22 CrossRefGoogle Scholar
  7. 7.
    Balabanov, T., Zankinski, I., Barova, M.: Distributed Evolutionary computing migration strategy by incident node participation. In: Lirkov, I., Margenov, S.D., Waśniewski, J. (eds.) LSSC 2015. LNCS, vol. 9374, pp. 203–209. Springer, Cham (2015). doi: 10.1007/978-3-319-26520-9_21 CrossRefGoogle Scholar
  8. 8.
    Eiben, A.E., Raué, P.-E., Ruttkay, Z.: Genetic algorithms with multi-parent recombination. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 78–87. Springer, Heidelberg (1994). doi: 10.1007/3-540-58484-6_252 CrossRefGoogle Scholar
  9. 9.
    Ting, C.-K.: On the mean convergence time of multi-parent genetic algorithms without selection. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 403–412. Springer, Heidelberg (2005). doi: 10.1007/11553090_41 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Delyan Keremedchiev
    • 1
    Email author
  • Petar Tomov
    • 1
  • Maria Barova
    • 1
  1. 1.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations