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Adaptation using hybridized genetic crossover strategies

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  • Sysi-Aho, Marko
  • Chakraborti, Anirban
  • Kaski, Kimmo

Abstract

We present a simple game which mimics the complex dynamics found in many natural and social systems. Players modify their strategies periodically, depending on their performances. We propose that the agents use hybridized one-point genetic crossover mechanism, inspired by genetic evolution in biology, to modify the strategies and replace the bad strategies. We study the performances of the agents under different conditions and investigate how they adapt themselves in order to survive or be the best, by finding new strategies using the highly effective mechanism we proposed. We introduce the measure of total utility of the system and use it to study the efficiency and dynamics of the game.

Suggested Citation

  • Sysi-Aho, Marko & Chakraborti, Anirban & Kaski, Kimmo, 2003. "Adaptation using hybridized genetic crossover strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 701-709.
    • Handle: RePEc:eee:phsmap:v:322:y:2003:i:c:p:701-709
    DOI: 10.1016/S0378-4371(02)01827-7
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    Citations

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    Cited by:

    1. Lustosa, Bernardo C. & Cajueiro, Daniel O., 2010. "Constrained information minority game: How was the night at El Farol?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1230-1238.
    2. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    3. Kiran Sharma & Anamika & Anindya S. Chakrabarti & Anirban Chakraborti & Sujoy Chakravarty, 2017. "The Saga of KPR: Theoretical and Experimental developments," Papers 1712.06358, arXiv.org.
    4. Ren, F. & Zhang, Y.C., 2008. "Trading model with pair pattern strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5523-5534.

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