Description: 这是一个非常简单的遗传算法源代码，代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -This is a very simple genetic algorithm source code, code guarantees minimal, in fact there is no need Checking. For a specific application to amend this code, users only need to change the constant definition and the definition of "evaluation function" can. Attention to the design of the code for the maximum, the objective function can only take positive; and function and adapt to the individual was no difference between the value. The system uses ratio choices, the essence model, single point of hybridization and mutation uniform. If Gaussian variation replacement uniform variation, may be a better result. Code no graphics, or even no screen output, mainly to ensure that the platforms to the high portability. Readers can get ftp.uncc.edu. Contents coe / evol prog.c the document Platform: |
Size: 3758 |
Author:nokia8 |
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Description: 这是一个非常简单的遗传算法源代码，代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -This is a very simple genetic algorithm source code, code guarantees minimal, in fact there is no need Checking. For a specific application to amend this code, users only need to change the constant definition and the definition of "evaluation function" can. Attention to the design of the code for the maximum, the objective function can only take positive; and function and adapt to the individual was no difference between the value. The system uses ratio choices, the essence model, single point of hybridization and mutation uniform. If Gaussian variation replacement uniform variation, may be a better result. Code no graphics, or even no screen output, mainly to ensure that the platforms to the high portability. Readers can get ftp.uncc.edu. Contents coe/evol prog.c the document Platform: |
Size: 3072 |
Author: |
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Description: 利用小波变换检测突变点实验的实例，程序最后生成3个图像演示了该算法，分别为原数字信号、高斯函数作为基函数、高斯函数的一阶导数作为基函数的小波变换。-Mutation detection using wavelet transform examples of experimental points, the program generates the final three images to demonstrate the algorithm, namely, the original digital signal, Gaussian function as basis function, Gaussian function of the first order derivative as the base function of wavelet transform. Platform: |
Size: 1024 |
Author:ladan |
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Description: 采用低通性质的平滑函数为高斯函数，根据他的一阶导数、二阶导数作为小波基函数进行突变点分析。
-The use of low-pass nature of the smoothing function for the Gaussian function, according to his first derivative, second derivative wavelet basis function as a point mutation analysis. Platform: |
Size: 1024 |
Author:ladan |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。
-This is a very simple genetic algorithm source code, by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) as amended. Code to ensure that as little as possible, in fact, do not have errors. The application of a specific amendment to this code, the user can change the definition of constants and the definition of "evaluation function" can be. Note the code is designed for maximum value, in which the objective function can only take positive and function to adapt to individual values and there was no difference between values. The system uses the ratio of choice, the best model, a single point of hybridization and uniform mutation. If the variation of the replacement of uniform Gaussian mutation may be more effective. Code without any graphics, or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol documents obtained prog.c. Asked to enter the fi Platform: |
Size: 4096 |
Author:Kaavield |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。-This is a very simple genetic algorithm source code is determined by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure as little as possible, in fact there is no need troubleshooting. The application of a specific amendment to this code, the user simply by changing the definition of constants, and the definition of "evaluation function" can be. Note that the code is designed to seek maximum value, where the objective function can only take on positive values and the function values and individuals there is no difference between the values of adaptation. The system uses the ratio option, essence model, a single point of hybridization, and uniform mutation. If we replace the uniform Gaussian mutation variation, may get better results. Code without any graphics, or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file pr Platform: |
Size: 4096 |
Author:陈朋 |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。-This is a very simple genetic algorithm source code is determined by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure as little as possible, in fact there is no need troubleshooting. The application of a specific amendment to this code, the user simply by changing the definition of constants, and the definition of "evaluation function" can be. Note that the code is designed to seek maximum value, where the objective function can only take on positive values and the function values and individuals there is no difference between the values of adaptation. The system uses the ratio option, essence model, a single point of hybridization, and uniform mutation. If we replace the uniform Gaussian mutation variation, may get better results. Code without any graphics, or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file pr Platform: |
Size: 4096 |
Author:陈朋 |
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Description: ntroduction to Stochastic Search and Optimization, 2003
This program runs a GA with real-number coding. Elitism is used
and the mutation operator is simply the addition of a Gaussian
random vector to the non-elite elements.
The user is expected to set a variable expect_fn representing the
expected number of function evaluations allowed.-ntroduction to Stochastic Search and Optimization, 2003
This program runs a GA with real-number coding. Elitism is used
and the mutation operator is simply the addition of a Gaussian
random vector to the non-elite elements.
The user is expected to set a variable expect_fn representing the
expected number of function evaluations allowed. Platform: |
Size: 3072 |
Author:shahnaz |
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Description: 基于生物免疫系统的自适应学习、免疫记忆、抗体多样性及动态平衡维持等功能,提出一种动态多目标免疫
优化算法处理动态多目标优化问题.算法设计中,依据自适应ζ邻域及抗体所处位置设计抗体的亲和力,基于Pa-
reto控制的概念,利用分层选择确定参与进化的抗体,经由克隆扩张及自适应高斯变异,提高群体的平均亲和力,利
用免疫记忆、动态维持和Average linkage聚类方法,设计环境识别规则和记忆池,借助3种不同类型的动态多目标
测试问题,通过与出众的动态环境优化算法比较,数值实验表明所提出算法解决复杂动态多目标优化问题具有较大
潜力.-:A dynamic multi-objective immune optimization algorithm suitable for dynamic multi-objective
optimization problems is proposed based on the functions of adaptive learning, immune memory, antibody
diversity and dynamic balance maintenance, etc. In the design of the algorithm, the scheme of antibody af-
finity was designed based on the locations of adaptive-neighborhood and antibody antibodies participating
in evolution were selected by Pareto dominance. In order to enhance the average affinity of the population,
clonal proliferation and adaptive Gaussian mutation were adopted to evolve excellent antibodies. Further-
more, the average linkage method and several functions of immune memory and dynamic balance mainte-
nance were used to design environmental recognition rules and the memory pool. The proposed algorithm
was compared against several popular multi-objective algorithms by means of three different kinds of dy-
namic multi-objective benchmark problems. Simulations show Platform: |
Size: 499712 |
Author:王飞 |
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Description: 注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。-Note that the code is designed to seek maximum value, in which the objective function can only take positive and the function value and the individual is no difference between the fitness value. The system uses the rate selection, the essence of model, one point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation, may be more effective. Platform: |
Size: 3072 |
Author: |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -It is a very simple genetic algorithm source code, is Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure as little as possible, in fact, do not have to troubleshooting. The application of a specific amendment to this code, the user simply changes the definition of constants and the definition of "evaluation function" can be. Note that the code is designed to seek maximum value, in which the objective function can only take positive and the function value and the individual is no difference between the fitness value. The system uses the rate selection, the essence of model, single-point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation, may be more effective. Code without any graphic or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file prog.c obtained. Required input file sho Platform: |
Size: 8192 |
Author:李礼 |
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Description: 摘要：人口迁移算法模拟了人口随经济中心而转移和随人口压力增加而扩散的机制。主要针对该算法提出了一种改进的人口迁
移算法。该改进算法通过引入高斯变异算子和最速下降算子来改善人口迁移算法的收敛速度和全局收敛性，并对其收敛性进行了
证明。通过对函数的数值实验测试结果表明，改进的人口迁移算法的全局寻优能力和收敛速度较人口迁移算法均有所提高。
-Abstract: The migration algorithm simulation with the economic centers of population transfer and population pressures increase with the proliferation mechanism. Primarily for the algorithm, an improved method of population migration. Improved algorithm of the Gaussian mutation operator and steepest descent operator to improve the convergence rate of population migration and the global convergence, and its convergence proved. Numerical experiments on the function test results show that the improved migration capacity of the global optimization algorithm and convergence rate than both increased migration algorithm. Platform: |
Size: 229376 |
Author:杜政 |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码 的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用 Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu, 目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的 文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。 -This is a very simple genetic algorithm source code, by Denis Cormier (North Carolina State University) developed, Sita S. Raghavan (University of North Carolina at Charlotte) amendment. Code to ensure that as little as possible, in fact, do not have to troubleshoot. The application of a specific amendment to this code, the user simply changes the definition of constants and the definition of "evaluation function" button. Note that the code is designed to seek maximum value, where the objective function can only take positive and the function value and the individual is no difference between the fitness value. The system uses the ratio of choice, the essence of model, single-point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation, may get better results. Code without any graphic or even no screen output, mainly to ensure a high portability between platforms. Readers can ftp.uncc.edu, directory coe/evol file prog.c obtained. Requires the input f Platform: |
Size: 5120 |
Author:qinjian |
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Description: 这是一个基于实数编码的Fortran语言的遗传算法，并用MPI并行实现，里面提供了丰富的选择算子，交叉算子，变异算子可供选择，如选择算子有基于赌盘选择算子，无回放余数随机选择算子，变异算子有高斯变异，自适应变异等，此外还实行了一种策略可以有效地降低早熟的概率，本人还有一份非MPI版本的遗传算法，需要者可以联系。-This is a real-coded in Fortran language of genetic algorithms, and use MPI parallel implementation, which provides a rich selection operator, crossover operator, mutation operator to choose from, such as the selection operator are based gamble selection operator, no more than a few random playback selection operator, mutation operator with Gaussian mutation, adaptive mutation, in addition to the implementation of a strategy can effectively reduce the probability of premature, I have a non-MPI version of the genetic algorithm, for those who need to Contact. Platform: |
Size: 11264 |
Author:彭鑫 |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。-This is a very simple genetic algorithm source code, which is developed by Denis Cormier (North Carolina State University), the Sita S.Raghavan (the University of North Carolina at Charlotte) correction. Code to ensure that as little as possible, and in fact do not have to troubleshoot. This code of a specific application correction, the user can simply change the definition of the constants and can be defined "evaluation function". Attention to the design of the code is the maximum, where the objective function can only take positive and there is no difference between the value of the function and the individual s fitness. The system uses the ratio of choice, the essence of the model, a single point of hybridization and uniform variation. If replaced by Gaussian variation uniform mutation, possible to obtain better results. Code no graphics, or even no screen output, mainly to ensure high portability between platforms. Readers can get in the from the file prog.c ftp.uncc.edu, catalog Platform: |
Size: 3072 |
Author:jasondiao1983 |
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Description: 这是一个非常简单的遗传算法源代码，是由Denis Cormier (North Carolina State University)开发的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代码保证尽可能少，实际上也不必查错。对一特定的应用修正此代码，用户只需改变常数的定义并且定义“评价函数”即可。注意代码的设计是求最大值，其中的目标函数只能取正值；且函数值和个体的适应值之间没有区别。该系统使用比率选择、精华模型、单点杂交和均匀变异。如果用Gaussian变异替换均匀变异，可能得到更好的效果。代码没有任何图形，甚至也没有屏幕输出，主要是保证在平台之间的高可移植性。读者可以从ftp.uncc.edu,目录 coe/evol中的文件prog.c中获得。要求输入的文件应该命名为‘gadata.txt’；系统产生的输出文件为‘galog.txt’。输入的文件由几行组成：数目对应于变量数。且每一行提供次序——对应于变量的上下界。如第一行为第一个变量提供上下界，第二行为第二个变量提供上下界，等等。-This is a very simple genetic algorithm source code, by Denis Cormier (North Carolina State University) developed, Sita S.Raghavan (University of North Carolina at Charlotte) corrected. Code to ensure as little as possible, in fact, do not have to troubleshoot. For a specific application fix this code, users only need to change the definition of constants and the definition of "evaluation function" button. Note that the code is designed for the maximum, in which the objective function can only take positive values , and fitness function values and the individual is no difference between the values . The system uses the ratio selection, the essence of the model, a single point crossover and uniform mutation. If you replace the uniform mutation with Gaussian mutation may get better results. Code has no graphics, or even no screen output, mainly to ensure high portability between platforms. Readers can ftp.uncc.edu, catalog coe/evol files prog.c ge Platform: |
Size: 35840 |
Author:周成 |
Hits:
Search - gaussian mutation