遗传算法及其在渔饲料配方中的应用

The genetic algorithm and its application to feed diet of fishing

首先阐述了参数编码、初始群体、适应度函数、遗传操作以及算法控制参数等遗传算法基本原理,然后着重介绍其在渔饲料配方中的应用,在该应用中采用了实数编码、基于线性排名的选择、算术杂交和非一致变异等遗传算法方法。结果表明,在渔饲料配方优化中应用遗传算法要优于目前饲料配方软件中大多采用的常规应用数学的优化方法。主要体现在单位产量饲料配方中的成本价格前者要低于后者。由于遗传算法能够解决因子较多,非线性程度高的问题,从而得出的饲料配方能更好的符合相关鱼种的营养含量标准。

After elaborating the theory of the genetic algorithm, including the coding,initial population, fitness function, genetic operation and controls parameter of arithmetic, this paper presents its application to feed diet of fishing in detail. It adopts real coding, linear ranking selection, arithmetical crossover and nonconforming mutation. The results show that this algorithm is better than the optimization of common application mathematics in the feed diet of fishing. It mainly embodies that the unitage cost price of the former is lower than that of latter. Due to resolving the long degree non-linear problem with more factors, the diet by the genetic algortithm more well and truly accords with the nutrition tontent criterion of corresponding fish.