Издательство InTech, 2012. - 328 p.Genetic Algorithms (GAs) are global optimization techniques used in many real-life applications. They are one of several techniques in the family of Evolutionary Algorithms – algorithms that search for solutions to optimization problems by evolving better and better solutions.
A Genetic Algorithm starts with a population of possible solutions for the desired application. The best ones are selected to become parents and then, using genetic operators like crossover and mutation, offspring are generated. The new solutions are evaluated and added to the population and low-quality solutions are deleted from the population to make room for new solutions. The members of the population tend to get better with the increasing number of generations. When the algorithm is halted, the best member of the existing population is taken as the solution to the problem.
Genetic Algorithms have been applied in science, engineering, business and social sciences. A number of scientists have already solved many real-life problems using Genetic Algorithms. This book consists of 16 chapters organized in five sections.GAs in Automatic Control
Selection of Optimal Measuring Points Using Genetic Algorithm in the Process to Calibrate Robot Kinematic Parameters
Model Predictive Controller Employing Genetic Algorithm Optimization of Thermal Processes with Non-Convex Constraints
Enhancing Control Systems Response Using Genetic PID Controllers
Finite-Thrust Trajectory Optimization Using a Combination of Gauss Pseudospectral Method and Genetic Algorithm
Genetic Algorithm Application in Swing Phase Optimization of AK Prosthesis with Passive Dynamics and Biomechanics Considerations
GAs in Scheduling Problems
Genetic Algorithms Application to Electric Power Systems
Genetic Algorithms Implement in Railway Management Information System
GAs in Electrical and Electronics Engineering
Efficient VLSI Architecture for Memetic Vector Quantizer Design
Multiple Access System Designs via Genetic Algorithm in Wireless Sensor Networks
Genetic Algorithms in Direction Finding
Applications of Genetic Algorithm in Power System Control Centers
GAs in Pattern Recognition
Applying Genetic Algorithm in Multi Language’s Characters Recognition
Multi-Stage Based Feature Extraction Methods for Uyghur Handwriting Based Writer Identification
Towards the Early Diagnosis of Alzheimer’s Disease Through the Application of a Multicriteria Classification Model
GAs in Trading Systems
Portfolio Management Using Artificial Trading Systems Based on Technical Analysis

Genetic Algorithm Application for Trading in Market towar

Издательство InTech, 2012. - 328页。遗传算法（GA）是用于许多现实生活中的全球优化技术。它们是进化算法家族中的几种技术之一--通过进化出更好的解决方案来寻找优化问题的解决方案的算法。

遗传算法从所需应用的可能解决方案的群体开始。挑选出最好的方案作为亲本，然后使用交叉和变异等遗传算子，生成后代。新的解决方案被评估并添加到群体中，低质量的解决方案被从群体中删除，以便为新的解决方案提供空间。随着代数的增加，种群的成员往往会变得更好。当算法停止时，现有群体中的最佳成员被作为问题的解决方案。

遗传算法已被应用于科学、工程、商业和社会科学。一些科学家已经利用遗传算法解决了许多现实生活中的问题。本书由16个章节组成，分为五个部分。自动控制中的遗传算法

在校准机器人运动学参数的过程中使用遗传算法选择最佳测量点

采用遗传算法的模型预测控制器 优化具有非凸性约束的热过程

利用遗传PID控制器增强控制系统的响应能力

使用高斯伪谱法和遗传算法的组合进行有限推力轨迹优化

遗传算法在被动动力学和生物力学考虑的AK假体摆动阶段优化中的应用

遗传算法在日程安排问题中的应用

遗传算法在电力系统中的应用

遗传算法在铁路管理信息系统中的应用

遗传算法在电气和电子工程中的应用

记忆矢量量化器设计的高效VLSI架构

无线传感器网络中通过遗传算法的多路接入系统设计

遗传算法在测向中的应用

遗传算法在电力系统控制中心中的应用

遗传算法在模式识别中的应用

遗传算法在多语言字符识别中的应用

基于多阶段特征提取的维吾尔族书写者识别方法

通过应用多标准分类模型实现阿尔茨海默病的早期诊断

交易系统中的 GA

基于技术分析的人工交易系统的投资组合管理

遗传算法在市场交易中的应用