针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明...

图像分割的实例程序，其中的CDib类可以直接应用于其他的图像处理程序。-examples of image segmentation procedures, which the CDib categories can be directly applied to other image processing.

人脸定位实例是模式识别中一个比较复杂的技术 希望能帮到大家-Human Face Localization example is a more complicated pattern recognition technology hope to help everyone

This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox

人工智能经典问题TSP问题的Java实现，内含源程序和调试过程生成的文件-AI classic TSP Java realize that contains source and debugging process produces files

一个简单的遗传算法 采用实数编码、比例分配轮盘赌选择、杂交采用离散与算术双重杂交、一般性变异。-A simple genetic algorithm using real-coded, proportional allocation of roulette selection, hybrid discrete and numeracy double hybridization, general variation.

该算法采用另外一种策略，结合进化策略和遗传算法，达到优化的目地-The algorithm uses a different strategy, combining evolutionary strategy and genetic algorithm to optimize the purpose

微分进化算法DE是1995年由Rainer Storn和Kenneth Price首先提出。DE已被证明在求解过程中具有高效性、收敛性、鲁棒性等优点[5,6] 。它在许多优化问题中都表现出优于自适应模拟退火算法、POS 算法、GA算法的性能。DE利用实数值参数向量作为每一代的种群，它的自参考种群繁殖方案与其他优化算法不同。-DE was first proposed in 1995 by Rainer Storn and Kenneth Price and had been proven that it is efficient, convergent, and robust in the solution process. [5,6] In many optimize questions, its performance surpasses the adaptive simulated annealing algorithm, the POS algorithm, and the GA algorithm. DE gets each generation of population using the real value parameter vector, and the crucial idea of DE is a scheme for generating trial parameter vectors.

由java开发的软件包，里面有人工智能所用的很多东东，包括神经网络，支持向量机，决策树等分类和回归分析方法，集成化软件哦！-by java development package, which has artificial intelligence used by many of the Eastern, including neural networks, support vector machines, such as decision tree classification and regression analysis, integrated software Oh!

本文在VC++环境下用演化算法实现了非线性参数回归，实验结果表明，用此方法能够很好的实现非线性参数回归。-paper in VC environment using an evolutionary algorithm to achieve the nonlinear regression, experimental results indicate that this method can achieve good nonlinear regression.

nuralnetwork using back propegation and genatic algorithm implementation