Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
Résumé
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
Mots clés
• Mathematics of computing → Combinatorial optimization Target set selection combinatorial optimization biased randomkey genetic algorithm parameter tuning
• Mathematics of computing → Combinatorial optimization Target set selection
combinatorial optimization
biased randomkey genetic algorithm
parameter tuning
Domaines
Informatique [cs]
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Doerr_Krejca_Vu__Superior_Genetic_Algorithms_Target_Set_Selection_Power_Law__GECCO__2024_arxiv.pdf (244.44 Ko)
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