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New coevolution dynamic as an optimization strategy in group problem solving

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  • Francis Ferreira Franco

    (Federal University of Jataí (UFJ))

  • Paulo Freitas Gomes

    (Federal University of Jataí (UFJ))

Abstract

Coevolution on social models couples the time evolution of the network with the time evolution of the states of the agents. This paper presents a new coevolution dynamic allowing more than one rewiring on the network. We explore how this coevolution can be employed as an optimization strategy for problem-solving capability of task-forces. We use an agent-based model to study how this new coevolution dynamic can help a group of agents whose task is to find the global maxima of NK fitness landscapes. Each agent can replace more than one neighbor, and this quantity is a tunable parameter in the model. These rewirings are a way for the agent to obtain information from individuals that were not previously part of its neighborhood. Our results showed that this tunable coevolution can indeed produce gain on the computational cost under certain circumstances. At high average degree network and difficult landscape, the effect is complex. If the agent has a low fitness, 3 or 4 rewirings can bring some improvement. Graphical Abstract

Suggested Citation

  • Francis Ferreira Franco & Paulo Freitas Gomes, 2024. "New coevolution dynamic as an optimization strategy in group problem solving," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(12), pages 1-11, December.
    • Handle: RePEc:spr:eurphb:v:97:y:2024:i:12:d:10.1140_epjb_s10051-024-00828-8
    DOI: 10.1140/epjb/s10051-024-00828-8
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    References listed on IDEAS

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    1. Sandro M. Reia & Paulo F. Gomes & José F. Fontanari, 2019. "Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(9), pages 1-10, September.
    2. Gomes, P.F. & Fernandes, H.A. & Costa, A.A., 2022. "Topological transition in a coupled dynamics in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    3. Sandro M. Reia & Paulo F. Gomes & José F. Fontanari, 2019. "Individual decision making in task-oriented groups," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(5), pages 1-7, May.
    4. Daniel Barkoczi & Mirta Galesic, 2016. "Social learning strategies modify the effect of network structure on group performance," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
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    6. Ganco, Martin, 2017. "NK model as a representation of innovative search," Research Policy, Elsevier, vol. 46(10), pages 1783-1800.
    7. Yann Lucas Silva & Ariadne Andrade Costa, 2024. "Periodic boundary condition effects in small-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(7), pages 1-9, July.
    8. José F Fontanari, 2014. "Imitative Learning as a Connector of Collective Brains," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-7, October.
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