A Hybrid Optimization Approach for Multi-Generation Intelligent Breeding Decisions

Abstract

Multi-generation intelligent breeding (MGIB) decision-making is a technique used by plant breeders to select mating individuals to produce new generations and allocate resources for each generation. However, existing research remains scarce on dynamic optimization of resources under limited budget and time constraints. Inspired by advances in reinforcement learning (RL), a framework that integrates evolutionary algorithms with deep RL was proposed to fill this gap. The framework combines two modules: the Improved Look-Ahead Selection (ILAS) module and Deep Q-Networks (DQNs) module. The former employs a simulated annealing-enhanced estimation of the distribution algorithm to make mating decisions. Based on the selected mating individual, the latter module learns multi-generation resource allocation policies using DQN. To evaluate our framework, numerical experiments were conducted on two realistic breeding datasets, i.e., Corn2019 and CUBIC. The ILAS outperformed LAS on corn2019, increasing the maximum and mean population Genomic Estimated Breeding Value (GEBV) by 9.1% and 7.7%. ILAS-DQN consistently outperformed the baseline methods, achieving significant and practical improvements in both top-performing and elite-average GEBVs across two independent datasets. The results demonstrated that our method outperforms traditional baselines, in both generalization and effectiveness for complex agricultural problems with delayed rewards