Genetic Algorithm-optimized Deep Learning Models in Breeding Practices
In recent years,deep learning technologies have achieved significant breakthroughs across various do-mains,establishing themselves as the core tools for data-driven research.Particularly in the field of biological breeding,deep learning models offer powerful support for tasks such as trait prediction,gene selection,and func-tional annotation.However,due to the unique characteristics of biological data,such as high-dimensionality and a large amount of unlabeled data,traditional deep learning models often struggle with direct applications.To address this challenge,researchers have turned to optimizing model parameters using genetic algorithms.Serving as a global search method that simulates the natural evolutionary process,the genetic algorithm systematically explores the parameter space of models to identify optimal configurations.By integrating genetic algorithms,the perfor-mance of deep learning models in biological breeding tasks has been markedly enhanced,with improvements also seen in model generalizability.This study delves into the applications of genetic algorithm-optimized deep learning models in breeding,providing an in-depth analysis of the role and mechanism of genetic algorithms in model opti-mization,thereby offering researchers effective data analysis tools and methods for breeding.
NETL
NSTL
万方数据
维普
