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.
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 language | English |
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Journal | European Physical Journal B: Condensed Matter and Complex Systems |
Early online date | 18 Sept 2017 |
DOIs | |
Publication status | E-pub ahead of print - 18 Sept 2017 |
Keywords
- evolutionary theory
- optimisation
- agent-based model
- Information Theory
- Algorithms
- PHASE-CHANGE