基于递进制编码的遗传算法研究

首先分析了编码的进制数对遗传算法收敛速度的影响．分析结果表明，当种群不稳定时，高进制编码较低进制编码具有更快地搜索至最优区域的能力，当种群较为稳定时，低进制编码较高进制编码具有更快地逼近最优点的能力．然后提出了基于递进制的遗传算法，该算法能提高优化问题的收敛速度，在优化参数较多时，与单一进制编码的遗传算法相比具有明显的优势．仿真实例也验证了这一结论．

Research on Genetic Algorithm Based on Degressive Carry Number Encoding

The influence of encoding mechanism on the convergence of genetic algorithm is analyzed. High carry number encoding has the ability of faster searching to optimization area, compared with low carry number encoding while the population is unstable. Low carry number encoding has the ability of faster searching to optimization value, compared with high carry number encoding while the population is stable. Then, the genetic algorithm based on degressive carry number encoding is proposed. The algorithm can improve the convergence speed of optimization problems, and this advantage is obvious while the parameters are excessive. Simulations validate the conclusion.