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Information spread is an intriguing topic to study in network science, whichinvestigates how information, influence, or contagion propagate throughnetworks. Graph burning is a simplified deterministic model for how informationspreads within networks. The complicated NP-complete nature of the problemmakes it computationally difficult to solve using exact algorithms.Accordingly, a number of heuristics and approximation algorithms have beenproposed in the literature for the graph burning problem. In this paper, wepropose an efficient genetic algorithm called Centrality BAsedGenetic-algorithm (CBAG) for solving the graph burning problem. Considering theunique characteristics of the graph burning problem, we introduce novel geneticoperators, chromosome representation, and evaluation method. In the proposedalgorithm, the well-known betweenness centrality is used as the backbone of ourchromosome initialization procedure. The proposed algorithm is implemented andcompared with previous heuristics and approximation algorithms on 15 benchmarkgraphs of different sizes. Based on the results, it can be seen that theproposed algorithm achieves better performance in comparison to the previousstate-of-the-art heuristics. The complete source code is available online andcan be used to find optimal or near-optimal solutions for the graph burningproblem.

CBAG: An Efficient Genetic Algorithm for the Graph Burning Problem