Hybrid Genetic Algorithm and CMA-ES Optimization for RNN-Based Chemical Compound Classification
Zhenkai Guo,
Dianlong Hou,
Qiang He
Abstract:The compound classification strategies addressed in this study encounter challenges related to either low efficiency or accuracy. Precise classification of chemical compounds from SMILES symbols holds significant importance in domains such as drug discovery, materials science, and environmental toxicology. In this paper, we introduce a novel hybrid optimization framework named GA-CMA-ES which integrates Genetic Algorithms (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to train Recurrent … Show more
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