Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
arxiv(2024)
摘要
The target set selection problem (TSS) asks for a set of vertices such that
an influence spreading process started in these vertices reaches the whole
graph. The current state of the art for this NP-hard problem are three recently
proposed randomized search heuristics, namely a biased random-key genetic
algorithm (BRKGA) obtained from extensive parameter tuning, a max-min ant
system (MMAS), and a MMAS using Q-learning with a graph convolutional network.
We show that the BRKGA with two simple modifications and without the costly
parameter tuning obtains significantly better results. Our first modification
is to simply choose all parameters of the BRKGA in each iteration randomly from
a power-law distribution. The resulting parameterless BRKGA is already
competitive with the tuned BRKGA, as our experiments on the previously used
benchmarks show.
We then add a natural greedy heuristic, namely to repeatedly discard
small-degree vertices that are not necessary for reaching the whole graph. The
resulting algorithm consistently outperforms all of the state-of-the-art
algorithms.
Besides providing a superior algorithm for the TSS problem, this work shows
that randomized parameter choices and elementary greedy heuristics can give
better results than complex algorithms and costly parameter tuning.
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