采用遗传算法优化的深度学习模型在育种中的实践
Genetic Algorithm-optimized Deep Learning Models in Breeding Practices
田地1
作者信息
摘要
近年来,深度学习技术在多个领域都取得了显著的突破,成为数据驱动研究的核心工具.特别是在生物育种领域,深度学习模型为性状预测、基因选择和功能注释等任务提供了强大的支持.但由于生物数据的特殊性,如高维度、大量未标注数据等,传统的深度学习模型往往难以直接应用.为解决此问题,研究者开始考虑遗传算法对模型参数进行优化.遗传算法作为一种模拟自然进化过程的全局搜索方法,可以系统地探索模型的参数空间,寻找最佳的模型配置.通过结合遗传算法,深度学习模型在生物育种任务中的表现得到了显著的提升,同时也提高了模型的泛化能力.本研究针对采用遗传算法优化的深度学习模型在育种中的应用进行了系统性的探讨,深入解析了遗传算法在模型优化中的作用机制,并为育种研究者提供有效的数据分析工具和方法.
Abstract
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.
关键词
遗传算法/深度学习/育种/性状预测/模型优化Key words
Genetic algorithm/Deep learning/Breeding/Trait prediction/Model optimization引用本文复制引用
基金项目
河南省科技攻关项目(232102210141)
出版年
2024
NETL
NSTL
维普
万方数据