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==== Genetic operators ====

==== 遗传算子 ====

{{Main|Crossover (genetic algorithm)|Mutation (genetic algorithm)}}

{{Main|Crossover (genetic algorithm)|Mutation (genetic algorithm)}}

The next step is to generate a second generation population of solutions from those selected through a combination of genetic operators: crossover (also called recombination), and mutation.

The next step is to generate a second generation population of solutions from those selected through a combination of genetic operators: crossover (also called recombination), and mutation.

下一步是通过遗传操作的组合: 交叉(也称为重组)和变异，从那些被选中的解决方案中生成第二代种群。

下一步是通过遗传操作的组合: 交叉(也称为重组)和变异，从那些被选择出来的解中生成第二代种群。

For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated.

For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing a "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated.

 

Although reproduction methods that are based on the use of two parents are more "biology inspired", some research<ref>Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78&ndash;87. {{ISBN|3-540-58484-6}}.</ref><ref>Ting, Chuan-Kang (2005). "On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection". Advances in Artificial Life: 403&ndash;412. {{ISBN|978-3-540-28848-0}}.</ref> suggests that more than two "parents" generate higher quality chromosomes.

Although reproduction methods that are based on the use of two parents are more "biology inspired", some research<ref>Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78&ndash;87. {{ISBN|3-540-58484-6}}.</ref><ref>Ting, Chuan-Kang (2005). "On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection". Advances in Artificial Life: 403&ndash;412. {{ISBN|978-3-540-28848-0}}.</ref> suggests that more than two "parents" generate higher quality chromosomes.

Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes.

Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes.

虽然基于双亲的繁殖方法更具有“生物学启发性” ，但一些研究表明，超过两个“双亲”可以产生更高质量的染色体。

虽然基于双亲的繁殖方法更具有“生物学启发性” ，但一些研究表明<ref>Eiben, A. E. et al (1994). "Genetic algorithms with multi-parent recombination". PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78&ndash;87. {{ISBN|3-540-58484-6}}.</ref><ref>Ting, Chuan-Kang (2005). "On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection". Advances in Artificial Life: 403&ndash;412. {{ISBN|978-3-540-28848-0}}.</ref> ，超过两个的“双亲”可以产生更高质量的染色体。

These processes ultimately result in the next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure the genetic diversity of the subsequent generation of children.

These processes ultimately result in the next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure the genetic diversity of the subsequent generation of children.

It is worth tuning parameters such as the mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non-ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless elitist selection is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.

It is worth tuning parameters such as the mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non-ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless elitist selection is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required.

值得调整的参数，如变异概率，交叉概率和种群大小，以找到合理的设置正在进行的问题类。一个非常小的突变率可能会导致遗传漂变(在自然界中是非遍历性的)。过高的重组率可能导致遗传算法的早熟收敛。如果突变率过高，可能会导致失去好的解决方案，除非使用精英选择。适当的种群规模可以确保目前问题的足够遗传多样性，但如果设置的值大于所需值，则可能导致计算资源的浪费。

遗传算法种有一些需要调优的参数，如变异概率，交叉概率和种群大小，以找到适合特定问题类的合理设置。一个非常小的突变率可能会导致遗传漂变(在自然界中是非遍历性的)。过高的重组率可能导致遗传算法的早熟收敛。如果突变率过高，可能会导致失去好的解决方案，除非每一代保留最优的个体，不对其使用变异算子。适当的种群规模可以确保目前问题的足够遗传多样性，但如果设置的值大于所需值，则可能导致计算资源的浪费。

==== Heuristics ====

==== 启发式 ====

 

 

In addition to the main operators above, other [[heuristic]]s may be employed to make the calculation faster or more robust. The ''speciation'' heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature [[convergence (evolutionary computing)|convergence]] to a less optimal solution.<ref>{{Cite book|title=Handbook of Evolutionary Computation|last1=Deb|first1=Kalyanmoy|last2=Spears|first2=William M.|s2cid=3547258|publisher=Institute of Physics Publishing|year=1997|chapter=C6.2: Speciation methods}}</ref><ref>{{Cite book|title=Handbook of Natural Computing|last=Shir|first=Ofer M.|date=2012|publisher=Springer Berlin Heidelberg|isbn=9783540929093|editor-last=Rozenberg|editor-first=Grzegorz|pages=1035–1069|language=en|chapter=Niching in Evolutionary Algorithms|doi=10.1007/978-3-540-92910-9_32|editor-last2=Bäck|editor-first2=Thomas|editor-last3=Kok|editor-first3=Joost N.}}</ref>

In addition to the main operators above, other [[heuristic]]s may be employed to make the calculation faster or more robust. The ''speciation'' heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature [[convergence (evolutionary computing)|convergence]] to a less optimal solution.<ref>{{Cite book|title=Handbook of Evolutionary Computation|last1=Deb|first1=Kalyanmoy|last2=Spears|first2=William M.|s2cid=3547258|publisher=Institute of Physics Publishing|year=1997|chapter=C6.2: Speciation methods}}</ref><ref>{{Cite book|title=Handbook of Natural Computing|last=Shir|first=Ofer M.|date=2012|publisher=Springer Berlin Heidelberg|isbn=9783540929093|editor-last=Rozenberg|editor-first=Grzegorz|pages=1035–1069|language=en|chapter=Niching in Evolutionary Algorithms|doi=10.1007/978-3-540-92910-9_32|editor-last2=Bäck|editor-first2=Thomas|editor-last3=Kok|editor-first3=Joost N.}}</ref>

In addition to the main operators above, other heuristics may be employed to make the calculation faster or more robust. The speciation heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature convergence to a less optimal solution.

In addition to the main operators above, other heuristics may be employed to make the calculation faster or more robust. The speciation heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature convergence to a less optimal solution.

==== Termination ====

 

==== 终止 ====

This generational process is repeated until a termination condition has been reached. Common terminating conditions are:

This generational process is repeated until a termination condition has been reached. Common terminating conditions are:

This generational process is repeated until a termination condition has been reached. Common terminating conditions are:

This generational process is repeated until a termination condition has been reached. Common terminating conditions are:

此分代过程将重复，直到达到终止条件为止。常见的终止条件是:

此过程将重复进行，直到达到终止条件为止。常见的终止条件是:

* A solution is found that satisfies minimum criteria

* A solution is found that satisfies minimum criteria

* Fixed number of generations reached

* Fixed number of generations reached

* Allocated budget (computation time/money) reached

* Allocated budget (computation time/money) reached

* The highest ranking solution's fitness is reaching or has reached a plateau such that successive iterations no longer produce better results

* The highest ranking solution's fitness is reaching or has reached a plateau such that successive iterations no longer produce better results

* Manual inspection

* Manual inspection

* Combinations of the above

* Combinations of the above

 

== The building block hypothesis ==

== The building block hypothesis ==