2016/04/30 by Marco Alberto Javarone · 12 citations
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Focus (optics) #Game Theory and Applications #Heuristic #Metaheuristic Optimization Algorithms Research #Optimization problem #Population #Public good #Public goods game #Space (punctuation) #Travelling salesman problem #Vehicle Routing Optimization Methods #cs.GT #cs.NE #physics.soc-ph
paper · pdf · doi:10.1140/epjb/e2017-80346-6
published in The European Physical Journal B 90(9) (Springer Science+Business Media) · 17 pages, 5 figure. accepted for publication in Eur. Phys. J. B
arxiv created 2017/08/29 · arxiv updated 2017/08/30 · openalex publication_date 2017/09/01 · openalex created_date 2017/09/15 · openalex updated_date 2026/08/05
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.