Qing jixiang

Hi @tisimst , thanks for your suggestion, I may get where Inf comes. To be short: **When none of the initial candidate locates in the feasible domain, an Inf fp...

thanks tisimst , very detailed and clear answer. Since My parameter constrainted are state variables which comes from a black box function, there may not be any hope to refractor...

非常感谢正将的GA库 由于平时也会用到一些其他的优化程序，在这里提供一个数值优化算例的测试例子（由于我比较懒而且比较菜，就直接写这里好了），测试函数是10维的[Rosenbrock](https://zh.wikipedia.org/wiki/Rosenbrock%E5%87%BD%E6%95%B8)函数 测试结果可以作为一个Validation或者是性能参考 **测试案例:** 10 维 Rosenbrock 自变量x范围: -2, 2 个体数: 26 最大迭代代数: 1000 核数: 单核 **理论最优(小)值**: 0.0 **测试结果（10次运行）** GAFT: 最优解: [68.255, 8.982, 56.928, 11.604, 8.97, 9.827, 8.976, 8.746, 88.473,...

================================================ 2017/11/19更新: 之前对于Inspyred的优化设置有误，现在改进后重新绘图。目标函数运行次数在26000左右 对结果进行一个可视化: 这个结果是上面Rosenbrock函数测试最优解的结果，两个程序（基于Indpyred的此函数优化与基于GAFT的此函数优化）**分别跑10次**  这个结果如果我没记错的话，和MATLAB的GA优化最优值比较类似，可能MATLAB的会稍微好一点点 **程序用时测试** 数据收集方法: 写一个main()函数，外面包一个计时装饰器，比较糙 下面这个是程序跑5次的用时 测试平台: Surface Pro 3 I5 4G Ram Windows 10  附一下装饰器函数 ``` def func_get_runtime_decorator(func): """ 用于debug的函数装饰器: 测试函数的运行时间 :param func:...

====== Update Line ======= Hi @capaulson and @takluyver It has been verified that the following line in the _updatePsi_ function is a major(**more than 70% runtime!**) contributer to the runtime...

Hi @capaulson , I've update the testfunction in #23 , which includes the bench mark function tested in #20 Also, I've tried to make it faster through some ways(numba, numexpr)...

hi @tung-nd, do you have any clue now? it seems both TF8 and TF10 have duplicating inputs, while for TF8 i can safely remove duplications as the output are exactly...