Description: 遗传算法解决双变量的函数最优化问题，有按钮的界面，用bc所编，生动模拟遗传进化过程-genetic algorithm to solve the two- variable optimization function, the button interface, using bc prepared by the vivid simulation of the process of genetic evolution Platform: |
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Author:连宙辉 |
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Description: 遗传算法包括基本算法的程序及其在优化函数方面的应用-genetic algorithms include basic algorithm for the optimization process and its function in the application Platform: |
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Author:朱易 |
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Description: 4。《演化程序——遗传算法和数据编码的结合》，[英]米凯利维兹着 科学出版社 2000年第一版
本书分三个部分共16章分别介绍了:1.遗传算法的概念、数学原理及方法步骤 2.遗传算法和数据编码联系起来所构成的演化程序 3.演化程序面向一些实际问题的应用。
本书语言生动，结构合理，较少使用专业性术语和深涩词汇，适合面临优化问题的研究生、程序员、设计师、工程师及科研工作人员参考。-4. "Evolutionary process-- genetic coding algorithm and data integration," [E] Mikhail Liweici a 520-531 2000 version of the first book in three parts a total of 16 chapters were introduced : 1. Genetic Algorithm concept mathematical principles and methods of Step 2. genetic algorithms and data coding linked posed by the evolution of procedures 3. Evolution-oriented procedures some of the practical problems of application. The book vivid language, reasonable structure, and less use of professional terminology and vocabulary deep acerbic suitable for optimization problems facing graduate students, programmers, designers, engineers and research personnel. Platform: |
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Author:孙东 |
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Description: 遗传算法，是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法.遗传算法是一类可用于复杂系统优化的具有鲁棒性的搜索算法-Genetic algorithm, is a simulation of Darwinian natural selection to genetic selection and biological evolution of the computing model is a natural evolutionary process by simulating the optimal solution search methods. Is a kind of genetic algorithm can be used for optimization of complex systems is robust The search algorithm Platform: |
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Author:曹睿 |
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Description: < MATLAB遗传算法工具箱及应用>>介绍了如何在MATLAB中完成遗传算法的应用。遗传算法[Genetic Arithmatic，简称GA]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的高效全局寻优搜索算法。GA摒弃传统的搜索方式，模拟自然界生物进化过程，采用人工进化的方式对目标空间进行随机优化搜索。MATLAB是MATHWORKS公司的一套高性能的数值计算和可视化软件。MATLAB遗传算法工具箱及应用
-Genetic Algorithm [Genetic Arithmatic, referred to as GA] is based on natural selection and genetic theory, the process of biological evolution survival of the fittest rules and groups of chromosomes within the clearing-house mechanism of the random combination of efficient global optimization search algorithm. GA to abandon the traditional search methods to simulate the process of natural biological evolution, artificial evolution approach on the target stochastic optimization search space. Mathworks Inc. MATLAB is a high-performance numerical computation and visualization software. MATLAB genetic algorithm toolbox and its application Platform: |
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Author:吴晓晖 |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。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。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications. Platform: |
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Author:wzy |
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Description: 此文档是遗传算法原理加源代码。
生物的进化是一个奇妙的优化过程，它通过选择淘汰，突然变异，基因遗传等规律产生适应环境变化的优良物种。遗传算法是根据生物进化思想而启发得出的一种全局优化算法。 -This document is the principle of genetic algorithm source code increases. Biological evolution is a wonderful optimization process, it eliminated by choosing a sudden variation of genetic and other changes in the law to adapt to the environment arising from the fine species. Genetic algorithm is based on ideas of biological evolution and the inspiration derived from a global optimization algorithm. Platform: |
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Author:sunguili |
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Description: 简单函数优化的遗传算法程序 简单函数优化的遗传算法程序-Simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization procedure Platform: |
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Author:jianhuajuly |
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Description: 利用matlab编写的一些简单函数优化的遗传算法程序～-Matlab prepared to use some simple function of the genetic algorithm optimization process ~ Platform: |
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Author:何洪举 |
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Description: 将局部优化算子引入遗传算法求解TSP问题，以求提高算法的性能。具体措施是在标准遗传算法的最后阶段增加步，即对每代的最优个体进行一定次数的局部搜索，以求改善该最优个体。首先提出将反序一杂交法引入局部优化过程中。
同几种‘常用的局部优化力一法相比，反序一杂交法的性能最为突出。实验结果表明，该优化力一法能有效求解300个城市以内的
TSP问题。
-Will introduce a local optimization operator TSP problem genetic algorithm, in order to increase the performance of algorithm. Specific measures in the standard genetic algorithm to increase the final phase of step-by-step, that is optimal for each individual to carry out on behalf of a certain number of local search, in order to improve the best individual. First put forward the anti-sequence hybridization to introduce a local optimization process. With several ' common edge of a local optimization method, the anti-sequence of a hybridization of the most outstanding performance. Experimental results show that the optimization method can effectively force a solution of 300 cities within the TSP problem. Platform: |
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Author:JONE |
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Description: 介绍了遗传算法的基本原理与求解流程， 详细阐述了Matlab 遗传算法工具箱的使用方法， 并通过使用遗遗传算法工具箱对一个典型的函数优化问题进行求解， 验证了该工具箱在解决函数优化问题上的有效性与实用性性。
-Introduces the basic principles of genetic algorithms and solving process, elaborated on the use of the Matlab genetic algorithm toolbox, and a typical function optimization problem is solved by using genetic genetic algorithm toolbox, verify the toolbox to solve function optimization on the question of the validity and practicality. Platform: |
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Description: tinyos 蚁群算法(ant colony optimization, ACO)，又称蚂蚁算法，是一种用来在图中寻找优化路径的机率型算法。它由Marco Dorigo于1992年在他的博士论文中提出，其灵感来源于蚂蚁在寻找食物过程中发现路径的行为。蚁群算法是一种模拟进化算法,初步的研究表明该算法具有许多优良的性质.针对PID控制器参数优化设计问题,将蚁群算法设计的结果与遗传算法设计的结果进行了比较,数值仿真结果表明,蚁群算法具有一种新的模拟进化优化方法的有效性和应用价值-The tinyos ant colony algorithm (ant colony optimization, ACO), also known as ant algorithm the the probability type algorithm is a method for finding the optimal path in the graph. By Marco Dorigo in his doctoral thesis in 1992, inspired by the behavior of ants found in the process of looking for food path. Ant colony algorithm is a simulated evolutionary algorithm, preliminary studies show that the algorithm has many excellent properties for the PID controller parameters to optimize the design problem, the design of ant colony algorithm and genetic algorithm design compared with numerical simulation results The results show that the ant colony algorithm with a new simulated evolutionary optimization method effectiveness and value Platform: |
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Author:Sofunzhao |
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Description: In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Platform: |
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Author:Hutama Bramantyo |
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Description: An Initialization Procedure in Solving Optimal
Power Flow by Genetic Algorithm.With this procedure, one can start the optimization process (i.e., genetic algorithm) with a set of control variables, causing few or no violations of constraints. Platform: |
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Author:Praveen |
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Description: In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Platform: |
Size: 3072 |
Author:uja |
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Description: 遗传算法（Genetic Algorithm）是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。遗传算法可以解决多种优化问题，如：TSP问题、生产调度问题、轨道优化问题等，在现代优化算法中占据了重要的地位，本例使用遗传算法求解最优解。-Genetic Algorithm (Genetic Algorithm) is a simulation of Darwinian natural selection and genetic mechanism of biological evolution of computational models of biological evolution, the process is a method of searching the optimal solution by simulating natural evolution. Genetic algorithms can solve a variety of optimization problems, such as: TSP problem, production scheduling, track optimization problems in modern optimization algorithms to occupy an important position, in this case the use of genetic algorithm for optimal solutions. Platform: |
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Author:jiangsiqi |
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Description: In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Platform: |
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Author:reyhooon
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Description: 遗传算法（Genetic Algorithm, GA）起源于对生物系统所进行的计算机模拟研究。它是模仿自然界生物进化机制发展起来的随机全局搜索和优化方法，借鉴了达尔文的进化论和孟德尔的遗传学说。其本质是一种高效、并行、全局搜索的方法，能在搜索过程中自动获取和积累有关搜索空间的知识，并自适应地控制搜索过程以求得最佳解。(The genetic algorithm (Genetic Algorithm, GA) originated from the computer simulation of biological systems. It is a random global search and optimization method which is developed by imitating the natural evolution mechanism of the nature. It refers to Darwin's theory of evolution and Mendel's theory of heredity. Its essence is an efficient, parallel and global search method. It can automatically acquire and accumulate knowledge about search space during search process, and adaptively control the search process to get the best solution.) Platform: |
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Author:ZJN27 |
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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 - Optimization of process by genetic algorithm