On the Particle Swarm Optimization Algorithm. Using java language.

遗传算法 遗传算法简称GA(Genetic Algorithm)，在本质上是一种不依赖具体问题的直接搜索方法。遗传算法在模式识别、神经网络、图像处理、机器学习、工业优化控制、自适应控制、生物科学、社会科学等方面都得到应用。在人工智能研究中，现在人们认为“遗传算法、自适应系统、细胞自动机、混沌理论与人工智能一样，都是对今后十年的计算技术有重大影响的关键技术”。-Referred to as genetic algorithm genetic algorithm GA (Genetic Algorithm), in essence is a kind of do not rely on specific issues of direct search method. Genetic algorithm in pattern recognition, neural networks, image processing, machine learning, optimization of industrial control, adaptive control, biological sciences, social sciences and so on have been applied. Research in artificial intelligence, now that the " genetic algorithms, adaptive systems, cellular automata, chaos theory and artificial intelligence, are the next ten years of the computing technology have a major impact on the key technology."

使用BP网络进行模式识别,识别0－9的数字-Using the BP network pattern recognition, identifying the number of 0-9

西安电子科技大学模式识别原理与应用课件1-Xi" an University of Electronic Science and Technology Pattern Recognition Theory and Application of a courseware

神经网络的遗传算法的集合-the set of neural network Genetic Algorithm

MATLAB的遗传算法工具-MATLAB genetic algorithm tool

贝叶斯优化算法是一种新的演化算法，通过贝叶斯概率统计的知识来学习后代，可是使演化朝有利的方向前进，程序用C实现了贝叶斯优化算法。-Bayesian Optimization Algorithm is a new evolutionary algorithm, through Bayesian probability and statistics to learn the knowledge of future generations, but to enable the evolution towards a favorable direction, procedures C achieved a Bayesian Optimization Algorithm.

小波人工神经网络C语言实现(代码)，以及相关支持库，已调试，可运行．-Wavelet artificial neural network C language (code), and related support libraries, have been debug run.

This software was done in part for a textbook on AI I ve written called _The Basis of AI_ (tentative title, subject to change but not if I get my way). For details see: http://www.mcs.com/~drt/basisofai.html

米凯利维奇遗传算法-Mikerevic Genetic Algorithm

同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications