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此代码是实数编码遗传算法的Matlab例程。用了精英保留策略选择和轮盘赌选择法，叉操作采用中间重组方式。-This code is a real-coded genetic algorithm Matlab routines. Retention policies with the elite selection and roulette wheel selection method, fork operation mode using the middle of restructuring.
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fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-fga.m the main program for the genetic algorithm binary Gray encoding, roulette wheel method based on non-linear ranking selection, uniform crossover and mutation operations, but al
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采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作.
-Binary Gray encoding, roulette wheel method based on non-linear ranking selection, uniform crossover and mutation operations, but also introduces the inversion operation.
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实现简单的进化算法，实数编码，轮盘赌选择方式，单点交叉，均匀变异。-Simple evolutionary algorithms, real coding, roulette wheel selection method, single-point crossover, uniform mutation.
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说明: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Descr iption: fga.m genetic algorithm for the main program binary Gray encoding, roulette wheel method based on linear ranking selection, uniform crossover and mutation operator
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说明: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Descr iption: fga.m genetic algorithm for the main program binary Gray encoding, roulette wheel method based on linear ranking selection, uniform crossover and mutation operato
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说明: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Descr iption: fga.m genetic algorithm for the main program binary Gray encoding, roulette wheel method based on linear ranking selection, uniform crossover and mutation operato
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遗传算法求解TSP问题，采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Genetic algorithm for solving TSP problem, using the binary Gray code, based on the roulette wheel method of non-linear ranking selection, uniform crossover, mutation, but also introduce
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roulette wheel selection method in matlab
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/求下列元素的最大值：目标函数max=f(x1,x2)=x1*x1+x2*x2 取值范围在{1，2，3，4，5，6，7}
//本算法在选择时使用：轮盘赌选择法、交叉时使用：单点交叉。
-A maximum of the following elements: objective function Max = f (x1, x2) x1+ x2 = x1** x2.Scope in,2,3,4,5,6,7 {1}This algorithm is used when the choice
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