The content presents two modifications to the biased random-key genetic algorithm (BRKGA) for solving the target set selection (TSS) problem, which is an NP-hard graph optimization problem.
The first modification is to choose the BRKGA parameters randomly during the run, using a power-law distribution. This avoids the need for costly parameter tuning, as done in the original BRKGA. The second modification is a simple greedy heuristic called reverseMDG, which aims to reduce the size of valid target sets by greedily removing low-degree vertices.
The authors show that the BRKGA with these two modifications, called fastBRKGA+rev, consistently outperforms the state-of-the-art algorithms, including the original tuned BRKGA, the max-min ant system (MMAS), and the MMAS with Q-learning and a graph convolutional network. This is achieved without any costly offline computations.
The results demonstrate that easy adjustments can significantly improve the quality of TSS heuristics. The insights on using power-law distributed parameter choices and simple problem-specific heuristics are not limited to the TSS problem and can be applied to other optimization problems as well.
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