Description: ：介绍遣传算法的基本原理和Matlab的遗传算法优化工具箱(GAOT)，分析了优化工具函数。探讨Matlab遗传算法工具箱在
参数优化和非线性规划中的应用。通过优化实例，说明遗传算法是一种具有良好的全局寻优性能的优化方法。用Maflab语
言及Maflab语言编制的优化工具箱进行优化设计具有语言简单、函数丰富、用法比较灵活、编程效率高等特点。-: Removal algorithm introduce the basic principles and Matlab Genetic Algorithm Optimization Toolbox (GAOT), an analysis of function optimization tool. Explore the Matlab Genetic Algorithm Toolbox in the parameter optimization and nonlinear programming applications. Through optimization examples to illustrate the genetic algorithm is a good global optimization method to optimize performance. Maflab language and using language Maflab Optimization Toolbox to optimize the design of the language is simple, function-rich, and using more flexible programming and high efficiency. Platform: |
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Author:icyrock |
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Description: 简要阐述了遗传算法的基本原理，并对O0PQ0R 遗传算法工具箱（"0SP）的参数进行
了详细的介绍。探讨了O0PQ0R 遗传算法工具箱在参数优化和非线性规划中的应用，实例证明了遗
传算法在参数优化和非线性规划中的可行性。-Briefly described the basic principles of genetic algorithms and genetic algorithms O0PQ0R Toolbox ( 0SP) the parameters in detail. O0PQ0R explored the genetic algorithm toolbox in the parameter optimization and nonlinear programming applications, examples of proven genetic algorithm in parameter optimization and nonlinear programming feasibility. Platform: |
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Author:dh |
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Description: 排课问题是一个有约束的、多目标的组合优化问题，并且已经被证明是一个NP完全问题。
遗传算法借鉴生物界自然选择和自然遗传机制，使用群体搜索技术，尤其是用于处理传统搜索方法难以解决的复杂的和非线性的问题。经过近40年的发展，遗传算法在理论研究和实际应用中取得了巨大的成功，本文将遗传算法用于排课问题的求解，首先讨论了排课问题中的影响因素、主要约束条件、求解目标和难点，并用数学模型完整地描述了排课问题。其次对多个模糊排课目标进行了定量分析，建立了排课优化目标空间。针对排课问题研究了染色体编码方式以及遗传算子的设计，提出了适应度函数的计算方法。最后对排课问题进行了实验。实验结果表明，其过程的目标值跟踪显示，算法稳健趋优，所得结果令人满意。-Course Scheduling problem is a constrained, multi-objective optimization problem, and has proven to be a NP complete problem.
Genetic algorithms reference biosphere and the natural genetic mechanism of natural selection, using the group search technology, particularly the traditional search methods for handling complex and difficult to solve nonlinear problems. After nearly 40 years of development, the genetic algorithm in the theoretical study and practical application was a great success, this paper genetic algorithm for solving the course timetabling problem, first discussed the impact of factors in the course arrangement, the main constraints, to solve goals and difficulties, and a complete mathematical model to describe the course arrangement. Arranging multiple fuzzy goals followed by a quantitative analysis, the optimal target Arranging space. Arranging for the Study of the chromosome coding and genetic operators design, proposed fitness function is calculated. Finally, the co Platform: |
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Author:张林杰 |
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Description: 遗传算法在非线性数值优化方面有着很强的生命力 ,适于波束赋形阵列天线的综合。Matlab 具有很强的数值计算能力和数据图视功能 ,经过二十多年的发展 ,逐渐成为工程师们进行数值优化的首选计算机语言。以一部具有余割平 方波束的雷达搜索天线阵为例 ,采用遗传算法对其馈电相位进行优化 ,优化结果与目标吻合。同时给出其主要节点的 Matlab程序。-Nonlinear numerical optimization genetic algorithm has a strong vitality, suitable for antenna array beamforming integrated. Matlab has a strong numerical ability and visual function data plan, after 20 years of development, engineers have become the preferred numerical optimization computer language. To a beam with cosecant square antenna array radar search, for example, the genetic algorithm to optimize their feed phase to optimize the results coincide with the target. The main nodes are given Matlab program. Platform: |
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Author:victor728 |
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Description: 基于遗传算法的OFDM自适应资源分配算法MATLAB源码
OFDM自适应资源分配问题（载波、功率等），是一个既含有离散决策变量，又含有连续决策变量的非线性优化模型，且含有较为复杂的非线性约束，因此适合采用智能优化算法进行求解。
-Adaptive genetic algorithm-based resource allocation algorithm for OFDM OFDM MATLAB source adaptive resource allocation problem (carrier, power, etc.), is a decision with both a discrete variable, but also with continuous decision variables of the nonlinear optimization model, and contains more complex non-linear constraints, so smart optimization algorithm for solving. Platform: |
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Author:jack |
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Description: matlab用法 主要用于线性规划，非线性规划，解决优化问题，作出最合理的决策等遗传算法程序-matlab usage is mainly used for linear programming, nonlinear programming to solve optimization problems, make the most rational decision-making, genetic algorithm Platform: |
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Author:刘旭 |
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Description: 基于遗传算法和非线性规划的函数寻优算法在一组等式或者不等式的约束下求极值。
基于遗传算法和非线性规划的函数寻优算法
在一组等式或者不等式的约束下求极值。-Based on genetic algorithm and nonlinear programming function optimization algorithm in a set of equations or inequality constraints for extreme value.
Based on genetic algorithm and nonlinear programming function optimization algorithm
In a set of equations or inequality constraints for extreme value.
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Author:王进 |
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Description: 本例应用遗传算法实现了对于一些非线性、多模型、多目标的函数优化问题，用其它优化方法较难求解，而遗传算法可以方便的得到较好的结果。-In this case the genetic algorithm is applied to implement for some nonlinear, model, multi-objective function optimization problem, with other optimization methods are difficult to solve, and the genetic algorithm can easily get good results. Platform: |
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Author:周建 |
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Description: 快速遗传剩余静校正方法(FGRS)
关键词： 互相关剩余静校正 遗传算法剩余静校正 快速遗传剩余静校正方法 大剩余静校正量
复杂地质地区（如复杂山地、山前带等），在野外静校正、折射或层析静校正等之后，仍可能存在大剩余静校正量，影响叠加效果。转换波剩余静校正量往往也较大，是转换波地震资料处理的难题之一。针对上述问题，研发了快速遗传剩余静校正方法，其特点是：
地表一致性
组合算法（互相关＋遗传算法）
非线性反演
快速高效
全局寻优
可解决大的纵波剩余静校正量问题(主要针对中高频量，可部分解决小的中低频量问题)
可部分解决转换波剩余静校正量问题
实际数据处理效果较好，对大剩校量的解决优于主流商业软件-fast genetic residual static correction method (FGRS)
Keywords : cross-correlation residual statics residual statics fast genetic algorithm genetic residual statics method large residual statics
Complex geological areas ( such as complex mountain , piedmont zone , etc. ) , in the field statics , refraction statics or chromatography , etc. After that, there may still be a large residual statics , the impact of overlay effect . Converted wave residual statics often larger problem is one of the converted wave seismic data processing . In response to these problems , the fast development of genetic residual static correction method , which is characterized by :
Surface consistency
Combination algorithm ( cross-correlation+ genetic algorithm )
Nonlinear inversion
Fast and efficient
Global optimization
Solve large longitudinal wave residual statics problem ( mainly for high frequency volume can partially solve the problem of small amount of low-frequenc Platform: |
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Description: 遗传算法的实现,在非线性优化中会用到,在人工智能中也会用到.-Genetic algorithm will be used in nonlinear optimization, also used in artificial intelligence Platform: |
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Author:anny |
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Description: 基于MATLAB的遗传算法。遗传算法是以自然选择和遗传理论为基础，将生物进化过程中适者生存规则与群体内部染色体的随机信息交换机制相结合算法。它提供了一种求解非线性、多模型、多目标等复杂系统优化问题的通用框架。-MATLAB Genetic Algorithms. Genetic algorithm based on natural selection and genetic theory, the process of biological evolution and the survival of the fittest rules of random information exchange mechanism within the group chromosome combined algorithm. It provides for solving nonlinear, multi-model, multi-objective optimization problems of complex systems such as a common framework.
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Author:梁家理 |
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Description: 根据遗传算法和BP神经网络理论，在matlab软件中编程实现基于遗传算法优化的BP神经网络非线性系统拟合算法。-According to the theory of genetic algorithm and BP neural network, in the matlab software programming to realize BP neural network nonlinear systems based on genetic algorithm optimization fitting method. Platform: |
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Author:汪杰 |
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Description: 对于未知的非线性函数，仅通过函数的输入输出数据难以准确寻找函数极值，这类问题可以通过神经网络结合遗传算法求解，利用神经网络的非线性拟合能力和遗传算法的非线性寻优能力寻找函数极值。-For unknown nonlinear function, only through the function of input and output data is difficult to accurately find the function extreme value, this kind of problem can be through the neural network combined with genetic algorithm, using the nonlinear fitting ability of the neural network and nonlinear optimization ability of genetic algorithm to find the function extreme value. Platform: |
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Author:汪杰 |
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Description: 遗传算法提供了求解非线性规划的通用框架，它不依赖于问题的具体领域。遗传算法的优点是将问题参数编码成染色体后进行优化， 而不针对参数本身， 从而不受函数约束条件的限制； 搜索过程从问题解的一个集合开始， 而不是单个个体， 具有隐含并行搜索特性， 可大大减少陷入局部最小的可能性。而且优化计算时算法不依赖于梯度信息，且不要求目标函数连续及可导，使其适于求解传统搜索方法难以解决的大规模、非线性组合优化问题。(Genetic algorithm provides a general framework for solving nonlinear programming, which does not depend on the specific problem domain. The advantage of genetic algorithm is that the problem parameters are encoded into chromosomes for optimization, rather than the parameters themselves. The search process starts from a set of problem solutions, rather than a single individual, and has the implicit parallel search feature, which can greatly reduce the possibility of falling into the local minimum. Moreover, the algorithm does not rely on gradient information and does not require the objective function to be continuous and differentiable, which makes it suitable for solving large-scale and nonlinear combinatorial optimization problems that are difficult to be solved by traditional search methods.) Platform: |
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Author:FZenjoys |
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Search - Genetic algorithm nonlinear optimization