急求遗传算法相关的英文翻译和原文啊

急求遗传算法相关的英文翻译和原文啊
急求遗传算法相关的英文翻译和原文啊

急求遗传算法相关的英文翻译和原文啊
Genetic algorithm is defined
Genetic algorithm is from representative problems could potentially solution set of a population began, and a population is by a gene encoding a certain number of individual component. Each individual is actually chromosome with characteristics of the entity. Chromosome as genetic material, namely, the main carrier of collection of multiple genes, its internal representations (namely genotype) is a certain genetic combinations, it had decided the individual shapes of external performance, such as black hair is characterized by chromosomes control these features of certain genetic combinations decision. Therefore, in a need to start to genotype phenotype realized from the mapping namely coding work. Because imitates the genetic code of work is very complex, we often simplified, such as binary coding, early generation population have later, according to the survival of the fittest and of the survival of the fittest principle, generational evolution to produce more and better approximate solutions, in every generation, according to the problems of the individual fitness size selection field individuals, and by nature genetics, genetic operator undertakes assorted crossover and mutation, produce represents a new solution set of population. This process will result in population like natural evolution as kid generation population than previous generations more adapted to the environment, the optimal individuals last population after decoding, can be as approximate optimal solution.
Genetic algorithm is the fundamental operational process is as follows:
A) initialization: set evolution algebra counter t = 0, set maximum evolution algebra t, randomly generated M an individual as the initial group P (0).
B) the individual assessment: calculation group P (t) in the fitness of each individual.
C) choose operation: will choose the operator role in groups. Choose the objective is to optimize the individual directly to the next generation or through genetic pairing cross produce new individual genetic again to the next generation. Select operator is built in groups of the individual fitness evaluation based on.
D) crossover operation; Will crossover operator role in groups. So-called cross refers to the two father generation individual part of the structure to replace restructured and generating new individual operation. Genetic algorithm plays a role as the core is crossover operator.
E) variation operation: will mutation operator role in groups. Or a group of individuals string of certain genes seat gene value change.
Group P (t) after selection, crossover and mutation computation get next generation group P (t 1).
F) conditions for the termination judgment: if the tT, the one with the evolutionary process have been with the maximum fitness individual as optimal solution output, terminate the calculation.
遗传算法定义
遗传算法是从代表问题可能潜在的解集的一个种群开始的,而一个种群则由经过基因编码的一定数目的个体组成.每个个体实际上是染色体带有特征的实体.染色体作为遗传物质的主要载体,即多个基因的集合,其内部表现（即基因型）是某种基因组合,它决定了个体的形状的外部表现,如黑头发的特征是由染色体中控制这一特征的某种基因组合决定的.因此,在一开始需要实现从表现型到基因型的映射即编码工作.由于仿照基因编码的工作很复杂,我们往往进行简化,如二进制编码,初代种群产生之后,按照适者生存和优胜劣汰的原理,逐代演化产生出越来越好的近似解,在每一代,根据问题域中个体的适应度大小选择个体,并借助于自然遗传学的遗传算子进行组合交叉和变异,产生出代表新的解集的种群.这个过程将导致种群像自然进化一样的后生代种群比前代更加适应于环境,末代种群中的最优个体经过解码,可以作为问题近似最优解.
遗传算法的基本运算过程如下:
a)初始化:设置进化代数计数器t=0,设置最大进化代数T,随机生成M个个体作为初始群体P(0).
b)个体评价:计算群体P(t)中各个个体的适应度.
c)选择运算:将选择算子作用于群体.选择的目的是把优化的个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代.选择操作是建立在群体中个体的适应度评估基础上的.
d)交叉运算；将交叉算子作用于群体.所谓交叉是指把两个父代个体的部分结构加以替换重组而生成新个体的操作.遗传算法中起核心作用的就是交叉算子.
e)变异运算:将变异算子作用于群体.即是对群体中的个体串的某些基因座上的基因值作变动.
群体P(t)经过选择、交叉、变异运算之后得到下一代群体P(t 1).
f)终止条件判断:若tT,则以进化过程中所得到的具有最大适应度个体作为最优解输出,终止计算.