Description: 多目标遗传算法程序
to run Demo files, is to run SGALAB_demo_*.m
what s new:
1) Multiple-Objective GAs
VEGA
NSGA
NPGA
MOGA
2) More TSP mutation and Crossover methods
PMX
OX
CX
EAX
Boolmatrix
3) More selection methods
Truncation
tornament
stochastic
4) mutation methods
binary single point
int/real single point
5) encoding/decoding methods
binary
integer/real
messy
gray
DNA
permuation
to fix the plot bugs for 4001 , download this file and replace old files.
-Multi-objective genetic algorithm program to run Demo files, is to run SGALAB_demo_*. m what s new: 1) Multiple-Objective GAsVEGANSGANPGAMOGA2) More TSP mutation and Crossover methodsPMXOXCX EAXBoolmatrix3) More selection methodsTruncationtornamentstochastic4) mutation methodsbinary single pointint/real single point5) encoding/decoding methodsbinaryinteger/realmessygrayDNA permuationto fix the plot bugs for 4001, download this file and replace old files. Platform: |
Size: 79872 |
Author:馨竹 |
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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: |
Size: 6146048 |
Author:吴晓晖 |
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Description: 遗传算法是一种模拟生物进化机制的随机全局优化搜索方法，具有很强的全局优化能力及鲁棒性。遗传算法属于直接搜索法，对适应函数基本无限制，既不要求连续，也不要求函数可微，而且不需要初始信息可以寻求全局最优解克服了单纯形算法初始条件影响大，易陷入局部最小等缺点，操作方便，速度快，不需要复杂的规则，且可用于多目标寻优，在解空间进行高效启发式搜索，可以提高运算速度。-The genetic algorithm is one simulation organic evolution mechanism stochastic global optimization reconnaissance method, has the very strong global optimization ability and robustness. The genetic algorithm belongs to the direct search method, to adapts the function basically unlimited, also does not request continual, also does not request the function differentiable, moreover did not need the initial information to be possible to seek the globally optimal solution to overcome the simplex algorithm initial condition to affect in a big way, easy to fall into is partially smallest and so on shortcomings, the ease of operation, the speed is quick, did not need the complex rule, and might use in the multi-objective optimizations, carried on the highly effective heuristic search in the solution space, might raise the operating speed. Platform: |
Size: 3072 |
Author:大同小异 |
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Description: 用遗传算法求解随机模型的C++实现，绝对能够运行成功的-Genetic algorithm with stochastic models of the C++ to achieve, definitely be able to run successful Platform: |
Size: 32768 |
Author:希瑞 |
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Description: 用于求解约束优化问题的算法，算法为差分进化/遗传算法/微粒群算法的融合。对于“[7] T. P. Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000”中给出的13个标准测试函数，均能得到问题最优解。如有任何疑问，请于http://2shi.phphubei.com.cn/index.php发帖询问，本人将详细解答。-Solving constrained optimization problems for the algorithm, Algorithm for Differential Evolution/Genetic Algorithm/Particle Swarm Optimization integration. For " [7] TP Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., Vol. 4, no. 3, pp. 284-294, Sep. 2000" given in 13 standard test functions, the optimal solution can be the problem. If you have any questions, please post http://2shi.phphubei.com.cn/index.php asked, I will answer in detail. Platform: |
Size: 37888 |
Author:李剑 |
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Description: JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.-JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques. Platform: |
Size: 11746304 |
Author:jim |
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Description: This a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. -This is a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. Platform: |
Size: 18432 |
Author:gao |
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Description: Global Optimization Toolbox provides methods that search for global solutions to problems that contain multiple
maxima or minima. It includes global search, multistart, pattern search, genetic algorithm, and simulated
annealing solvers. You can use these solvers to solve optimization problems where the objective or constraint
function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or
black-box functions with undefined values for some parameter settings. Platform: |
Size: 1024 |
Author:saswata |
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Description: 遗传算法是一类随机优化算法，但它不是简单的随机比较搜索，而是通过对染色体的评价和对染色体中基因的作用，有效地利用已有信息来指导搜索有希望改善优化质量的状态。-Genetic algorithms are a class of stochastic optimization algorithm, but it is not a simple comparison of random search, but through the evaluation of chromosomes and genes on the chromosomes in the role, effective use of existing information to guide the search for promising to improve the quality of the optimized state. Platform: |
Size: 2048 |
Author:鲁旭锋 |
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Description: 遗传算法的描述。遗传算法是一种很经典的随机化过程的算法，在实际应用中应用广泛，效果良好。-A description of the genetic algorithm. The genetic algorithm is a classical stochastic process of the algorithm, widely used in practical applications to good effect. Platform: |
Size: 2048 |
Author:王贺 |
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Description: 该算法是用随机模拟，遗传算法和神经网络相结合的混合算法求解随机期望模型的。-The algorithm is simulated using a stochastic, combining genetic algorithm and neural network hybrid algorithm for solving stochastic expected value model. Platform: |
Size: 4096 |
Author:高建炳 |
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Description: 随机共振过程中，针对参数a和b的优化处理，此处使用的是遗传算法。-Stochastic resonance process, the parameters a and b for the optimization, genetic algorithm is used here. Platform: |
Size: 1024 |
Author:王姐姐 |
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Description: 求解非线性方程组方法有经典算法以及近年来流行的遗传算法．牛顿法及其改进形式,但是此类算法的收敛性在很大程度上依赖于初始点的选择，对于某些非线性方程组容易导致求解失败 为了克服经典算法的缺点，设计了求解非线性方程组的混合遗传算法,但依然对方程组和编码方法有很高要求。PSO是受到鸟群或者鱼群社会行为的启发而形成的一种基于种群的随机优化技术。它是一类随机全局优化技术，通过粒子间的相互作用发现复杂搜索空间中的最优区域。该算法是一种基于群体智能的新型演化计算技术，具有简单易实现、设置参数少、全局优化能力强等优点。粒子群优化算法已在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等许多领域取得了成功的应用。 本函数包带函数，测试函数与使用简介-Methods for solving nonlinear equations and the recent popular classical algorithm genetic algorithm. Newton s method and its improved form, but such convergence of the algorithm is largely dependent on the choice of the initial point, for some nonlinear equations solved easily lead to failure In order to overcome the shortcomings of classical algorithms designed for solving nonlinear Equations hybrid genetic algorithm, but still on the equations and the encoding method has high requirements. PSO is social behavior by birds or fish inspired the formation of a population-based stochastic optimization techniques. It is a class of stochastic global optimization technique, through complex interaction between particles found in the search space optimum area. The algorithm is a novel based on swarm intelligence evolutionary computation techniques, with a simple and easy to achieve, set few parameters, global optimization ability, etc.. Particle swarm optimization in function optimizatio Platform: |
Size: 15360 |
Author: |
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Description: System reliability analysis and optimization are important to efficiently utilize available resources and to develop an optimal system design architecture. System reliability optimization has been solved by using optimization techniques including meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research field of the reliability optimization wherein the redundancy, the component reliability,
or both are to be determined. In recent years, a broad class of stochastic meta-heuristics, such as simulated annealing, genetic algorithm, tabu search, ant colony, and particle swarm optimization paradigms,has been developed for reliability-redundancy optimization of systems. Platform: |
Size: 339968 |
Author:ali |
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Description: 遗传算法（Genetic Algorithm，GA）是通过对自然界中生物的遗传和优胜劣汰的进化过程进行模拟与抽象，进而形成的一种自适应全局随机优化搜索方法。遗传算法只需提供目标函数作为寻优信息，它从某一随机生成的初始群体出发，经过选择、交叉和变异等遗传操作后对个体进行适应度评价，保留适应度较强的个体遗传到子代种群中，经过多次的迭代计算求得最优个体，即问题的最优解。本程序采用遗传算法可求解微网优化运行。-Genetic Algorithm is an adaptive global by nature, genetic and biological survival of the fittest evolutionary process simulation and abstract, and thus the formation of stochastic optimization search method. Just provide genetic algorithm optimization objective function as an information, which is generated from a random initial population of departure, after genetic manipulation, such as selection, crossover and mutation of individuals fitness evaluation, retained a strong individual genetic fitness to child on behalf of the population, after several iterations of computing the optimal solution to get the optimal individual, that is the problem. This program uses a genetic algorithm to solve the optimal operation of the micro-network. Platform: |
Size: 2048 |
Author:刘向 |
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Description: matlab环境下遗传算法（GA）同时优化随机共振（SR）参数a、b源程序-The source code of optimization of stochastic resonance parameters using genetic algorithm under the environment of MATLAB Platform: |
Size: 2048 |
Author:崔伟成 |
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Search - stochastic genetic algorithm