Solving Optimization Problems by the Public Goods Game

Marco Javarone

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
17 Downloads (Pure)

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.
Original languageEnglish
JournalEuropean Physical Journal B: Condensed Matter and Complex Systems
Early online date18 Sept 2017
DOIs
Publication statusE-pub ahead of print - 18 Sept 2017

Keywords

  • evolutionary theory
  • optimisation
  • agent-based model
  • Information Theory
  • Algorithms
  • PHASE-CHANGE

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