变焦佳点集遗传算法

通过使用数论佳点集理论与方法构造出佳点交又算子，佳点集遗传算法(GGA)具有更快的收敛速度和精度，且避免了常见的早期收敛现象，但是二进制编码的佳点集遗传算法在位串长度确定的情况下无法克服二进制与实数之间的映射误差。针对二进制编码遗传算法存在从最高位到最低位依次收敛的多米诺现象，提出含有变焦因子的佳点集遗传算法来变相增加位串编码长度以期缩小该映射误差，提高搜索效率和求解精度。通过不同维数下的Benchmark测试函数的仿真结果表明，改进的算法具有全局收敛、求解精度和搜索效率高的优点。

Good Point Set Genetic Algorithm with Zooming Factor

Good point set genetic algorithm has superiority in convergence speed, accuracy and overcome premature effectively by using the good point operator which is based on the principle of set in number theory. However, when the length of chromosome is fixed, the discretization error is inevitable. Aiming at the domino phenomenon of convergence from the highest position to lowest position of binary coding in good point set genetic algorithm, a zooming factor was proposed to lengthen the length of chromosome indirectly to minimize the discretization error, so the search efficiency and solution accuracy are improved as a result hhe simulation results based on Benchmark test function of different dimensions verify that the proposed good point set algorithm with zooming factor has the advantage of global convergence, high precision solution and search efficiency.