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演化算法 Evolutionary Algorithms (查看源代码)

2020年5月21日 (四) 11:35的版本

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{{Artificial intelligence}}
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In [[artificial intelligence]] (AI), an '''evolutionary algorithm''' ('''EA''') is a [[subset]] of [[evolutionary computation]],<ref name="EVOALG">{{cite article|last=Vikhar|first=P. A.|title=Evolutionary algorithms: A critical review and its future prospects|doi=10.1109/ICGTSPICC.2016.7955308|journal=Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)|location= Jalgaon|year= 2016|pages=261-265|isbn=978-1-5090-0467-6}}</ref> a generic population-based [[metaheuristic]] [[optimization (mathematics)|optimization]] [[algorithm]]. An EA uses mechanisms inspired by [[biological evolution]], such as [[reproduction]], [[mutation]], [[genetic recombination|recombination]], and [[natural selection|selection]]. [[Candidate solution]]s to the [[optimization problem]] play the role of individuals in a population, and the [[fitness function]] determines the quality of the solutions (see also [[loss function]]). [[Evolution]] of the population then takes place after the repeated application of the above operators.
      
在[[人工智能]]领域，演化算法是演化计算的子集<ref name="EVOALG">{{cite article|last=Vikhar|first=P. A.|title=Evolutionary algorithms: A critical review and its future prospects|doi=10.1109/ICGTSPICC.2016.7955308|journal=Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)|location= Jalgaon|year= 2016|pages=261-265|isbn=978-1-5090-0467-6}}</ref> ，是一种基于群体的元启发式最优化算法。演化算法使用到了各种模拟[[生物演化]]机制的操作，比如繁衍、变异、重组、选择等。在群体中每一个个体都是问题的备选解，而适应度函数用于计算出每一个解的质量（亦即个体对于环境的适应度）。演化就是在不断在群体中进行繁衍、变异、重组、选择这些操作进而找出最优解的过程。
 
在[[人工智能]]领域，演化算法是演化计算的子集<ref name="EVOALG">{{cite article|last=Vikhar|first=P. A.|title=Evolutionary algorithms: A critical review and its future prospects|doi=10.1109/ICGTSPICC.2016.7955308|journal=Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)|location= Jalgaon|year= 2016|pages=261-265|isbn=978-1-5090-0467-6}}</ref> ，是一种基于群体的元启发式最优化算法。演化算法使用到了各种模拟[[生物演化]]机制的操作，比如繁衍、变异、重组、选择等。在群体中每一个个体都是问题的备选解，而适应度函数用于计算出每一个解的质量（亦即个体对于环境的适应度）。演化就是在不断在群体中进行繁衍、变异、重组、选择这些操作进而找出最优解的过程。
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Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying [[fitness landscape]]. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of [[microevolution|microevolutionary processes]] and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor.<ref name="VLSI">{{cite book|last=Cohoon|first=J|display-authors=etal|title=Evolutionary algorithms for the physical design of VLSI circuits|url= https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf|journal=Advances in Evolutionary Computing: Theory and Applications|publisher= Springer, pp. 683-712, 2003|isbn=978-3-540-43330-9|date=2002-11-26}}</ref> In fact, this computational complexity is due to fitness function evaluation. [[Fitness approximation]] is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems{{Citation needed|date=June 2018}}; therefore, there may be no direct link between algorithm complexity and problem complexity.
      
演化算法通常会对各种优化问题得出很好的近似解，因为演化算法不会做出任何假设，对每一个问题都一视同仁。演化算法通常应用于求解最优化问题，在生物建模方面一般来说只会用在研究微观演化过程以及基于细胞过程的规划模型。在大多数演化算法的应用中，计算复杂度都是最大的障碍<ref name="VLSI">{{cite book|last=Cohoon|first=J|display-authors=etal|title=Evolutionary algorithms for the physical design of VLSI circuits|url= https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf|journal=Advances in Evolutionary Computing: Theory and Applications|publisher= Springer, pp. 683-712, 2003|isbn=978-3-540-43330-9|date=2002-11-26}}</ref>。事实上，这种计算复杂度来源于[[适应度函数]]。简化适应度函数、计算近似的适应度，是克服这一障碍的方法之一。由于简单的演化算法也往往可以解决复杂的优化问题，因此，算法的复杂度应该与问题的复杂度没有直接关系。
 
演化算法通常会对各种优化问题得出很好的近似解，因为演化算法不会做出任何假设，对每一个问题都一视同仁。演化算法通常应用于求解最优化问题，在生物建模方面一般来说只会用在研究微观演化过程以及基于细胞过程的规划模型。在大多数演化算法的应用中，计算复杂度都是最大的障碍<ref name="VLSI">{{cite book|last=Cohoon|first=J|display-authors=etal|title=Evolutionary algorithms for the physical design of VLSI circuits|url= https://www.ifte.de/mitarbeiter/lienig/cohoon.pdf|journal=Advances in Evolutionary Computing: Theory and Applications|publisher= Springer, pp. 683-712, 2003|isbn=978-3-540-43330-9|date=2002-11-26}}</ref>。事实上，这种计算复杂度来源于[[适应度函数]]。简化适应度函数、计算近似的适应度，是克服这一障碍的方法之一。由于简单的演化算法也往往可以解决复杂的优化问题，因此，算法的复杂度应该与问题的复杂度没有直接关系。
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==实现==
 
==实现==
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Step One: Generate the initial [[population]] of [[individual]]s randomly. (First generation)
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Step Two: Repeat the following regenerational steps until termination:
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#Evaluate the [[Fitness function|fitness]] of each individual in the population (time limit, sufficient fitness achieved, etc.)
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#Select the fittest individuals for [[reproduce|reproduction]]. (Parents)
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# [[Breed]] new individuals through [[crossover (genetic algorithm)|crossover]] and [[mutation (genetic algorithm)|mutation]] operations to give birth to [[offspring]].
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# Replace the least-fit individuals of the population with new individuals.
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第一步：随机生成第一代群体。
 
第一步：随机生成第一代群体。
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# 计算新个体的适应度。
 
# 计算新个体的适应度。
 
# 淘汰掉群体中适应度低的个体。
 
# 淘汰掉群体中适应度低的个体。
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==类型==
 
==类型==
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Similar techniques differ in [[genetic representation]] and other implementation details, and the nature of the particular applied problem.
   
演化算法的具体技术可以按照遗传信息表达方式、实现细节、以及特定问题的特定处理分类。
 
演化算法的具体技术可以按照遗传信息表达方式、实现细节、以及特定问题的特定处理分类。
 
* 遗传算法：这是演化算法中最常用的类型。这种技术应用于求解最优解是一组数字的问题。
 
* 遗传算法：这是演化算法中最常用的类型。这种技术应用于求解最优解是一组数字的问题。
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* 遗传规划（Genetic programming）：这种技术用于生成一段计算机程序，其适应度是这些计算机程序解决某计算问题的能力。
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* 遗传规划 Genetic programming ：这种技术用于生成一段计算机程序，其适应度是这些计算机程序解决某计算问题的能力。
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* 演化规划（Evolutionary programming）：与遗传规划类似，但其求解的计算机程序的结构是固定的，可以演化的是一些数值参数。
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* 演化规划 Evolutionary programming ：与遗传规划类似，但其求解的计算机程序的结构是固定的，可以演化的是一些数值参数。
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* 基因表达规划（Gene expression programming）：类似于遗传编程，基因表达规划同样以计算机程序群体来进行演化计算，但这种计算机程序虽然大小不同，却都编码在相同固定的线性染色体中。
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* 基因表达规划 Gene expression programming ：类似于遗传编程，基因表达规划同样以计算机程序群体来进行演化计算，但这种计算机程序虽然大小不同，却都编码在相同固定的线性染色体中。
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* 演化策略（Evolution strategy）：这种技术以实数向量作为个体，而且使用了自适应的变异率。
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* 演化策略 Evolution strategy ：这种技术以实数向量作为个体，而且使用了自适应的变异率。
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* 差分演化（Differential evolution）：这种算法以向量的差为基础，因此特别适合数值最优化问题。
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* 差分演化 Differential evolution ：这种算法以向量的差为基础，因此特别适合数值最优化问题。
 
* 神经演化：类似于遗传规划，但是基因组表示的是人工神经网络的结构和连接权重，这种基因组编码可以是直接编码也可以是间接编码。
 
* 神经演化：类似于遗传规划，但是基因组表示的是人工神经网络的结构和连接权重，这种基因组编码可以是直接编码也可以是间接编码。
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* 学习分类器系统（Learning classifier system）：这种技术的解是一组分类器（一组规则画着条件）。密歇根-学习分类器系统在个体层面进行演化而匹兹堡-学习分类器系统使用分类器集合群体来演化。最开始的时候这些分类器都是0-1编码的，如今包含实数、神经网络、或者S-expression。其适应度一般是分类器系统应用于强化学习或有监督学习时的准确率。
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* 学习分类器系统 Learning classifier system ：这种技术的解是一组分类器（一组规则画着条件）。密歇根-学习分类器系统在个体层面进行演化而匹兹堡-学习分类器系统使用分类器集合群体来演化。最开始的时候这些分类器都是0-1编码的，如今包含实数、神经网络、或者S-expression。其适应度一般是分类器系统应用于强化学习或有监督学习时的准确率。
       
==与生物过程的比较==
 
==与生物过程的比较==
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A possible limitation{{According to whom|date=May 2013}} of many evolutionary algorithms is their lack of a clear [[genotype-phenotype distinction]]. In nature, the fertilized egg cell undergoes a complex process known as [[embryogenesis]] to become a mature [[phenotype]]. This indirect [[encoding]] is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the [[evolvability]] of the organism.<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] {{Webarchive|url=https://web.archive.org/web/20160603205252/http://www.ofria.com/pubs/2009CluneEtAl.pdf |date=2016-06-03 }}. ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> Such indirect (a.k.a. generative or developmental) encodings also enable evolution to exploit the regularity in the environment.<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref> Recent work in the field of [[artificial development|artificial embryogeny]], or artificial developmental systems, seeks to address these concerns. And [[gene expression programming]] successfully explores a genotype-phenotype system, where the genotype consists of linear multigenic chromosomes of fixed length and the phenotype consists of multiple expression trees or computer programs of different sizes and shapes.<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>{{Synthesis inline|date=May 2013}}
      
对于演化算法来说，一个可能的局限可能是缺少对基因型和表现型的明确区分。在大自然中，一个受精卵会历经一个成为胚胎发育的过程，然后成为一个成熟的表现型。自然选择以表现型而非基因型为选择标准。这种间接编码被认为可以使得演化过程更健壮（降低致命突变的概率），并提高器官的进化能力<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] {{Webarchive|url=https://web.archive.org/web/20160603205252/http://www.ofria.com/pubs/2009CluneEtAl.pdf |date=2016-06-03 }}. ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> 。这种间接编码还使得演化能够充分利用环境的规律<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref>。最近有关人工胚胎学（artificial embryogeny）和人工发展系统（artificial developmental system）的工作也在寻求解决这一局限的方法。基因表达规划这一技术在基因型-表现型系统的探索中取得成功，其中基因型包括固定长度地线性多基因染色体，而表现型则包括了规模可变的多重表达式树或者计算机程序<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>。
 
对于演化算法来说，一个可能的局限可能是缺少对基因型和表现型的明确区分。在大自然中，一个受精卵会历经一个成为胚胎发育的过程，然后成为一个成熟的表现型。自然选择以表现型而非基因型为选择标准。这种间接编码被认为可以使得演化过程更健壮（降低致命突变的概率），并提高器官的进化能力<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] {{Webarchive|url=https://web.archive.org/web/20160603205252/http://www.ofria.com/pubs/2009CluneEtAl.pdf |date=2016-06-03 }}. ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> 。这种间接编码还使得演化能够充分利用环境的规律<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref>。最近有关人工胚胎学（artificial embryogeny）和人工发展系统（artificial developmental system）的工作也在寻求解决这一局限的方法。基因表达规划这一技术在基因型-表现型系统的探索中取得成功，其中基因型包括固定长度地线性多基因染色体，而表现型则包括了规模可变的多重表达式树或者计算机程序<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>。
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==相关技术==
 
==相关技术==
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[[Swarm intelligence|Swarm algorithms]]{{clarify|reason=Why are swarm algorithms associated with evolutionary ones?|date=January 2018}} include
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群算法包括：
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Swarm algorithms include
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群算法包括
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* [[Ant colony optimization]] – Based on the ideas of ant foraging by pheromone communication to form paths. Primarily suited for [[combinatorial optimization]] and [[graph theory|graph]] problems.
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* The runner-root algorithm (RRA) is inspired by the function of runners and roots of plants in nature<ref>F. Merrikh-Bayat, "The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature", ''Applied Soft Computing'', Vol. 33, pp. 292–303, 2015</ref>
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* [[Artificial bee colony algorithm]] – Based on the honey bee foraging behaviour. Primarily proposed for numerical optimization and extended to solve combinatorial, constrained and multi-objective optimization problems.
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* [[Bees algorithm]] is based on the foraging behaviour of honey bees. It has been applied in many applications such as routing and scheduling.
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* [[Cuckoo search]] is inspired by the brooding parasitism of the [[cuckoo]] species. It also uses [[Lévy flight]]s, and thus it suits for global [[optimization]] problems.
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* Electimize optimization - Based on the behavior of electron flow through electric circuit branches with the least electric resistance.<ref>{{Cite journal|last=Khalafallah Ahmed|last2=Abdel-Raheem Mohamed|date=2011-05-01|title=Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering|journal=Journal of Computing in Civil Engineering|volume=25|issue=3|pages=192–201|doi=10.1061/(ASCE)CP.1943-5487.0000080}}</ref>
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*[[Particle swarm optimization]] – Based on the ideas of animal flocking behaviour. Also primarily suited for [[numerical optimization]] problems.
      
* 蚁群优化：这种技术以蚂蚁觅食和通过信息素沟通寻路为基础，最早应用于组化优化和图相关问题。
 
* 蚁群优化：这种技术以蚂蚁觅食和通过信息素沟通寻路为基础，最早应用于组化优化和图相关问题。
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==其他基于群体的元启发式方法==
 
==其他基于群体的元启发式方法==
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*[[Hunting Search]] – A method inspired by the group hunting of some animals such as wolves that organize their position to surround the prey, each of them relative to the position of the others and especially that of their leader. It is a continuous optimization method<ref>{{cite journal |last1=Oftadeh |first1=R. |last2=Mahjoob |first2=M.J. |last3=Shariatpanahi |first3=M. |title=A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search |journal=Computers & Mathematics with Applications |date=October 2010 |volume=60 |issue=7 |pages=2087–2098 |doi=10.1016/j.camwa.2010.07.049 }}</ref> adapted as a combinatorial optimization method.<ref>{{cite journal |author1=Amine Agharghor |author2=Mohammed Essaid Riffi |year=2017 |title=First Adaptation of Hunting Search Algorithm for the Quadratic Assignment Problem |journal=Europe and MENA Cooperation Advances in Information and Communication Technologies |volume=520 |pages=263–267 |doi=10.1007/978-3-319-46568-5_27 |isbn=978-3-319-46567-8|series=Advances in Intelligent Systems and Computing }}</ref>
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* 狩猎搜索 Hunting Search ：根据自然中地狩猎行为提出，一群捕食者，如狼群，组织他们的位置来包围猎物。捕食者中每一个个体的位置会根据其他个体，尤其是领袖的位置调整。这是一个连续型优化方法<ref>{{cite journal |last1=Oftadeh |first1=R. |last2=Mahjoob |first2=M.J. |last3=Shariatpanahi |first3=M. |title=A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search |journal=Computers & Mathematics with Applications |date=October 2010 |volume=60 |issue=7 |pages=2087–2098 |doi=10.1016/j.camwa.2010.07.049 }}</ref>，后改进为组合优化方法<ref>{{cite journal |author1=Amine Agharghor |author2=Mohammed Essaid Riffi |year=2017 |title=First Adaptation of Hunting Search Algorithm for the Quadratic Assignment Problem |journal=Europe and MENA Cooperation Advances in Information and Communication Technologies |volume=520 |pages=263–267 |doi=10.1007/978-3-319-46568-5_27 |isbn=978-3-319-46567-8|series=Advances in Intelligent Systems and Computing }}</ref>。
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* 适应性维度搜索 Adaptive dimensional search ：不同于仿生元启发式技术，适应性维度搜索没有模仿任何大自然中的现象。它使用简单的性能导向方法，每一次迭代过程都会更新搜索维度率 search dimension ratio 。<ref>Hasançebi, O., Kazemzadeh Azad, S. (2015), "Adaptive Dimensional Search: A New Metaheuristic Algorithm for Discrete Truss Sizing Optimization", ''Computers and Structures'', 154, 1–16.</ref>。
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*[[Adaptive dimensional search]] – Unlike nature-inspired metaheuristic techniques, an adaptive dimensional search algorithm does not implement any metaphor as an underlying principle. Rather it uses a simple performance-oriented method, based on the update of the search dimensionality ratio (SDR) parameter at each iteration.<ref>Hasançebi, O., Kazemzadeh Azad, S. (2015), "Adaptive Dimensional Search: A New Metaheuristic Algorithm for Discrete Truss Sizing Optimization", ''Computers and Structures'', 154, 1–16.</ref>
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*[[Firefly algorithm]] is inspired by the behavior of fireflies, attracting each other by flashing light. This is especially useful for multimodal optimization.
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*[[Harmony search]] – Based on the ideas of musicians' behavior in searching for better harmonies. This algorithm is suitable for combinatorial optimization as well as parameter optimization.
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*[[Gaussian adaptation]] – Based on information theory. Used for maximization of manufacturing yield, [[mean fitness]] or [[average information]]. See for instance [[Entropy in thermodynamics and information theory]].
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*[[Memetic algorithm]] – A hybrid method, inspired by [[Richard Dawkins]]'s notion of a meme, it commonly takes the form of a population-based algorithm coupled with individual learning procedures capable of performing local refinements. Emphasizes the exploitation of problem-specific knowledge, and tries to orchestrate local and global search in a synergistic way.
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*Emperor Penguins Colony – A method inspired by the behavior of emperor penguins in their colony. The emperor penguins in the colony seek to create the appropriate heat and regulate their body temperature, and this heat is completely coordinated and controlled by the movement of the penguins.<ref>{{cite journal |last1=Harifi |first1=Sasan |last2=Khalilian |first2=Madjid |last3=Mohammadzadeh |first3=Javad |last4=Ebrahimnejad |first4=Sadoullah |title=Emperor Penguins Colony: a new metaheuristic algorithm for optimization |journal=Evolutionary Intelligence |date=25 February 2019 |volume=12 |issue=2 |pages=211–226 |doi=10.1007/s12065-019-00212-x }}</ref>
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* 狩猎搜索（Hunting Search）：根据自然中地狩猎行为提出，一群捕食者，如狼群，组织他们的位置来包围猎物。捕食者中每一个个体的位置会根据其他个体，尤其是领袖的位置调整。这是一个连续型优化方法，后改进为组合优化方法。
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* 适应性维度搜索（Adaptive dimensional search）：不同于仿生元启发式技术，适应性维度搜索没有模仿任何大自然中的现象。它使用简单的性能导向方法，每一次迭代过程都会更新搜索维度率（search dimension ratio）。
   
* 萤火虫算法：通过模仿萤火虫发光互相吸引的行为设计，特别适用于多峰函数的最优化求解。
 
* 萤火虫算法：通过模仿萤火虫发光互相吸引的行为设计，特别适用于多峰函数的最优化求解。
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* 和弦搜索（Harmony search）：基于音乐家探索更好的和弦的因为提出，适用于组合优化和参数优化。
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* 和弦搜索 Harmony search ：基于音乐家探索更好的和弦的因为提出，适用于组合优化和参数优化。
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* 高斯适应（Gaussian adaptation）：一种基于信息论的方法， 用于最大化成品率、平均适应度、平均信息量等。参考热力学和信息论中的熵
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* 高斯适应 Gaussian adaptation ：一种基于信息论的方法， 用于最大化成品率、平均适应度、平均信息量等。参考热力学和信息论中的熵
 
* 模因算法：这是一种混合方法，根据理查德·道金斯的模因概念提出。这通常在基于群体的算法中，给每一个个体加入学习行为使得他们可以进行局部优化。这种方法强调利用每个问题自身的特性，并调和局部和全局搜索。
 
* 模因算法：这是一种混合方法，根据理查德·道金斯的模因概念提出。这通常在基于群体的算法中，给每一个个体加入学习行为使得他们可以进行局部优化。这种方法强调利用每个问题自身的特性，并调和局部和全局搜索。
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* 帝企鹅群：一种受帝企鹅群的行为启发的方法。在群体中的帝企鹅会产生适应的热量并调整自身的体温，这种热量只受每只企鹅的移动来调整和控制。
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* 帝企鹅群：一种受帝企鹅群的行为启发的方法。在群体中的帝企鹅会产生适应的热量并调整自身的体温，这种热量只受每只企鹅的移动来调整和控制。<ref>{{cite journal |last1=Harifi |first1=Sasan |last2=Khalilian |first2=Madjid |last3=Mohammadzadeh |first3=Javad |last4=Ebrahimnejad |first4=Sadoullah |title=Emperor Penguins Colony: a new metaheuristic algorithm for optimization |journal=Evolutionary Intelligence |date=25 February 2019 |volume=12 |issue=2 |pages=211–226 |doi=10.1007/s12065-019-00212-x }}</ref>
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==案例==
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==案例==
    
In 2020, [[Google]] stated their AutoML-Zero can successfully rediscover classic algorithms such as the concept of neural networks.<ref>{{cite news |last1=Gent |first1=Edd |title=Artificial intelligence is evolving all by itself |url=https://www.sciencemag.org/news/2020/04/artificial-intelligence-evolving-all-itself |accessdate=16 April 2020 |work=Science {{!}} AAAS |date=13 April 2020 |language=en |archive-url=https://web.archive.org/web/20200416222954/https://www.sciencemag.org/news/2020/04/artificial-intelligence-evolving-all-itself |archive-date=16 April 2020 |url-status=dead }}</ref>
 
In 2020, [[Google]] stated their AutoML-Zero can successfully rediscover classic algorithms such as the concept of neural networks.<ref>{{cite news |last1=Gent |first1=Edd |title=Artificial intelligence is evolving all by itself |url=https://www.sciencemag.org/news/2020/04/artificial-intelligence-evolving-all-itself |accessdate=16 April 2020 |work=Science {{!}} AAAS |date=13 April 2020 |language=en |archive-url=https://web.archive.org/web/20200416222954/https://www.sciencemag.org/news/2020/04/artificial-intelligence-evolving-all-itself |archive-date=16 April 2020 |url-status=dead }}</ref>
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