IDEAS home Printed from https://ideas.repec.org/a/gam/jgames/v16y2025i1p5-d1562960.html
   My bibliography  Save this article

N-Tuple Network Search in Othello Using Genetic Algorithms

Author

Listed:
  • Hiroto Kuramitsu

    (Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan)

  • Kaiyu Suzuki

    (Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan)

  • Tomofumi Matsuzawa

    (Department of Information Sciences, Tokyo University of Science, Yamazaki, Chiba 278-8510, Japan)

Abstract

As one of the strongest Othello agents, Edax employs an n-tuple network to evaluate the board, with points of interest represented as tuples. However, this network maintains a constant shape throughout the game, whereas the points of interest in Othello vary with respect to game’s progress. The present study was conducted to optimize the shape of the n-tuple network using a genetic algorithm to maximize final score prediction accuracy for a certain number of moves. We selected shapes for 18-, 22-, 26-, 30-, 34-, 38-, 42-, and 46-move configurations, and constructed an agent that appropriately shapes an n-tuple network depending on the progress of the game. Consequently, agents using the n-tuple network developed in this study exhibited a winning rate of 75%. This method is independent of game characteristics and can optimize the shape of larger (or smaller) N-tuple networks.

Suggested Citation

  • Hiroto Kuramitsu & Kaiyu Suzuki & Tomofumi Matsuzawa, 2025. "N-Tuple Network Search in Othello Using Genetic Algorithms," Games, MDPI, vol. 16(1), pages 1-11, January.
    • Handle: RePEc:gam:jgames:v:16:y:2025:i:1:p:5-:d:1562960
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4336/16/1/5/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-4336/16/1/5/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    2. Jonatas B. C. Chagas & Julian Blank & Markus Wagner & Marcone J. F. Souza & Kalyanmoy Deb, 2021. "A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem," Journal of Heuristics, Springer, vol. 27(3), pages 267-301, June.
    3. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    4. Xiaoyu Yu & Jingyi Qian & Yajing Zhang & Min Kong, 2023. "Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
    5. Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    6. Gonçalves, José Fernando & Resende, Mauricio G.C., 2015. "A biased random-key genetic algorithm for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 246(1), pages 86-107.
    7. Dianne Villicaña-Cervantes & Omar J. Ibarra-Rojas, 2024. "Accessible location of mobile labs for COVID-19 testing," Health Care Management Science, Springer, vol. 27(1), pages 1-19, March.
    8. F. Stefanello & L. S. Buriol & M. J. Hirsch & P. M. Pardalos & T. Querido & M. G. C. Resende & M. Ritt, 2017. "On the minimization of traffic congestion in road networks with tolls," Annals of Operations Research, Springer, vol. 249(1), pages 119-139, February.
    9. Galrão Ramos, A. & Oliveira, José F. & Gonçalves, José F. & Lopes, Manuel P., 2016. "A container loading algorithm with static mechanical equilibrium stability constraints," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 565-581.
    10. Perea, Federico & Yepes-Borrero, Juan C. & Menezes, Mozart B.C., 2023. "Acceptance Ordering Scheduling Problem: The impact of an order-portfolio on a make-to-order firm’s profitability," International Journal of Production Economics, Elsevier, vol. 264(C).
    11. Sam Heshmati & Jannes Verstichel & Eline Esprit & Greet Vanden Berghe, 2019. "Alternative e-commerce delivery policies," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 217-248, September.
    12. Soares, Leonardo Cabral R. & Carvalho, Marco Antonio M., 2020. "Biased random-key genetic algorithm for scheduling identical parallel machines with tooling constraints," European Journal of Operational Research, Elsevier, vol. 285(3), pages 955-964.
    13. Pedro Pinacho-Davidson & Christian Blum, 2020. "Barrakuda : A Hybrid Evolutionary Algorithm for Minimum Capacitated Dominating Set Problem," Mathematics, MDPI, vol. 8(11), pages 1-26, October.
    14. Ghorashi Khalilabadi, S. M. & Roy, D. & de Koster, M.B.M., 2022. "A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout," ERIM Report Series Research in Management ERS-2022-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    15. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.
    16. Bernardo F. Almeida & Isabel Correia & Francisco Saldanha-da-Gama, 2018. "A biased random-key genetic algorithm for the project scheduling problem with flexible resources," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 283-308, July.
    17. Julliany S. Brandão & Thiago F. Noronha & Celso C. Ribeiro, 2016. "A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks," Journal of Global Optimization, Springer, vol. 65(4), pages 813-835, August.
    18. Fabrizio Altarelli & Alfredo Braunstein & Luca Dall’Asta & Caterina De Bacco & Silvio Franz, 2015. "The Edge-Disjoint Path Problem on Random Graphs by Message-Passing," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-18, December.
    19. Tangpattanakul, Panwadee & Jozefowiez, Nicolas & Lopez, Pierre, 2015. "A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite," European Journal of Operational Research, Elsevier, vol. 245(2), pages 542-554.
    20. Christophe Duhamel & Philippe Mahey & Alexandre X. Martins & Rodney R. Saldanha & Mauricio C. Souza, 2016. "Model-hierarchical column generation and heuristic for the routing and wavelength assignment problem," 4OR, Springer, vol. 14(2), pages 201-220, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgames:v:16:y:2025:i:1:p:5-:d:1562960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.