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Solving optimization problems by the public goods game

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  • Marco Alberto Javarone

    (School of Computer Science, University of Hertfordshire
    Dept. of Mathematics and Computer Science, University of Cagliari)

Abstract

We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.

Suggested Citation

  • Marco Alberto Javarone, 2017. "Solving optimization problems by the public goods game," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(9), pages 1-7, September.
    • Handle: RePEc:spr:eurphb:v:90:y:2017:i:9:d:10.1140_epjb_e2017-80346-6
    DOI: 10.1140/epjb/e2017-80346-6
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    Statistical and Nonlinear Physics;

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