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Slot Machines RTP Optimization with Genetic Algorithms

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Numerical Methods and Applications (NMA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8962))

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Abstract

Slot machine RTP optimization problem is usually solved by hand adjustment of the symbols placed on the game reels. By controlling the symbols distribution, it is possible to achieve the desired return to player percent (RTP). Some other parameters can also be adjusted (for example, the free spins frequency or the bonus game frequency). In this paper RTP optimization automation, based on genetic algorithms, is proposed.

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References

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Acknowledgements

This research is supported by AComIn “Advanced Computing for Innovation”, Grant 316087, sponsored by FP7 Capacity Programme, Research Potential of Convergence Regions (2012–2016), the European Social Fund and the Republic of Bulgaria, the Operational Programme Development of Human Resources 20072013, Grant BG051PO001-3.3.06-0048 from 04.10.2012.

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Authors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, acad. G. Bonchev Str, Block 2, 1113, Sofia, Bulgaria

    Todor Balabanov, Iliyan Zankinski & Bozhidar Shumanov

Authors
  1. Todor Balabanov
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  2. Iliyan Zankinski
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  3. Bozhidar Shumanov
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Corresponding author

Correspondence to Todor Balabanov .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Dimov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

  3. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

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© 2015 Springer International Publishing Switzerland

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Balabanov, T., Zankinski, I., Shumanov, B. (2015). Slot Machines RTP Optimization with Genetic Algorithms. In: Dimov, I., Fidanova, S., Lirkov, I. (eds) Numerical Methods and Applications. NMA 2014. Lecture Notes in Computer Science(), vol 8962. Springer, Cham. https://doi.org/10.1007/978-3-319-15585-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-15585-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15584-5

  • Online ISBN: 978-3-319-15585-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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