自适应遗传退火算法优化BP神经网络及其应用

以提高预测软件老化趋势为应用背景，提出一种新型自适应遗传退火算法（NAGSA）优化BP神经网络模型，该模型采用轮盘赌选择法与精英保留策略相结合的选择算子，在迭代后期通过模拟退火算法对适应度函数进行拉伸，相比传统的自适应遗传算法（AGA）在个体适应度较低时，能够非线性地自适应调节交叉概率和变异概率，从而对BP神经网络的权值和阈值优化并进行网络训练.对在线售书网站注入内存泄漏的代码使之老化，收集实验所需的老化数据进行仿真训练，实验结果表明，NAGSA-BP模型相比于传统遗传算法（GA）、传统自适应遗传算法（AGA）、传统自适应遗传退火算法（NGSA）优化的BP神经网络模型提高了预测精度和取得了优良的收敛效果，在该应用领域验证了本文方法的有效性.

Adaptive Genetic Annealing Algorithm for Optimizing BP Neural Network and its Application

In order to improve the prediction accuracy of software aging, a New Adaptive Genetic Simulated Annealing algorithm (NAGSA) is proposed to optimize the BP neural network prediction model. The model''s selection operator is combined with the elite retention strategy using the roulette selection method, stretching the fitness function by simulated annealing algorithm in the late iteration. Compared with the traditional Adaptive Genetic Algorithm (AGA), it can adaptively adjust the crossover probability and mutation probability nonlinearly when the individual fitness is low, thereby optimizing and weighting the BP neural network weights and thresholds, injecting a memory leak code into the online book-sending website to age it, collecting the aging data required for the experiment for simulation training. The experimental results show that the BP neural network model optimized by the NAGSA-BP model compared with the traditional Genetic Algorithm (GA), traditional AGA, and traditional Adaptive Genetic Simulated Annealing algorithm (NGSA) improves the prediction accuracy and achieves excellent results. The effectiveness of the proposed method is verified in this application field.