Genetic-guided GFlowNets: Advancing in Practical Molecular Optimization
  Benchmark
Hyeonah Kim Minsu Kim Sanghyeok Choi Jinkyoo Park
This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet
(Genetic GFN), which integrates an iterative genetic search into GFlowNet.
Genetic search effectively guides the GFlowNet to high-rewarded regions,
addressing global over-exploration that results in training inefficiency and
exploring limited regions. In addition, training strategies, such as rank-based
replay training and unsupervised maximum likelihood pre-training, are further
introduced to improve the sample efficiency of Genetic GFN. The proposed method
shows a state-of-the-art score of 16.213, significantly outperforming the
reported best score in the benchmark of 15.185, in practical molecular
optimization (PMO), which is an official benchmark for sample-efficient
molecular optimization. Remarkably, ours exceeds all baselines, including
reinforcement learning, Bayesian optimization, generative models, GFlowNets,
and genetic algorithms, in 14 out of 23 tasks.