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第142行： − 这种演化过程的许多方面都是随机的。由于重组和突变而改变的信息片段是随机选择的。另一方面，选择运算符可以是确定性的，也可以是随机的。在后一种情况下，适合度较高的个体比适合度较低的个体有更高的机会被选中，但通常即使是体质较弱的个体也有机会成为父本或生存下来。+ − − − == Evolutionary algorithms and biology == − == Evolutionary algorithms and biology == − + − − − − − − 第164行：
第156行： − 遗传算法提供了与动力系统理论相关的生物系统和系统生物学模型的方法，因为它们被用来预测系统的未来状态。这只是一种生动的(但也许是误导性的)方式，提醒人们注意生物学发展的有序、控制良好和高度结构化的特征。+ 第184行：
第176行： − 这一观点的优点是认识到没有发育的中央控制；生物体的发育是细胞内部和细胞之间局部相互作用的结果。在我们看来，关于程序开发并行的最有前途的想法似乎是那些指出细胞内的进程与现代计算机的低级操作之间明显相似的思想。因此，生物系统就像计算机器，处理输入信息来计算下一个状态，这样生物系统比经典的动力系统更接近于计算。+ 第194行：
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第214行： − + − == Notable practitioners == − 著名从业人员 第230行：
第222行： − + − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − + − − − − == Conferences == − − == Conferences == − − 会议 第312行：
第253行： − + − + − 计算机械协会 遗传与进化计算会议 + − + − − − − − − − − − − − − − − − − − == See also == − − == See also == − − 参见 − − − − − − * [[Adaptive dimensional search]] − 适应性多维研究 − − − * [[Artificial development]] − − 人工发展 − − * [[Autoconstructive]] − − 自动建设性 − − * [[Developmental biology]] − − 发展性生物学 − − * [[Digital organism]] − − 数字化生物 − − * [[Estimation of distribution algorithm]] − − 分布算法估计 − − * [[Evolutionary robotics]] − 演化机器人 − − − * [[Evolved antenna]] − − 演化天线 − − * [[Fitness approximation]] − − 适应值近似 − − * [[Fitness function]] − − 适应值函数 − − * [[Fitness landscape]] − 适应度景观 − − * [[Genetic operators]] − − 遗传算子 − − * [[Grammatical evolution]] − − 文法演化 − − * [[Human-based evolutionary computation]] − − 人类演化计算 − − * [[Inferential programming]] − − 推断编程 − − * [[Interactive evolutionary computation]] − − 互动演化计算 − − * [[List of digital organism simulators]] − − 数字化有机体模拟器表 − * [[Mutation testing]] − − 变异测试 − − * [[No free lunch in search and optimization]] − − 研究和优化没有免费的午餐 − − * [[Program synthesis]] − − − 程序综合 − * [[Test functions for optimization]] − 优化测试函数 − − − * [[Universal Darwinism]] − − 普适达尔文主义 − − − − − == External links == − − == External links == − − 外部链接 − − *[https://plato.stanford.edu/entries/information-biological/#InfEvo/ Article in the Stanford Encyclopedia of Philosophy about Biological Information (English)] − + + + + + + + + + + + + + + + + + + + + + + + + − + − == Bibliography == − 参考书目 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − + + − − − == References == − − == References == − − 参考资料 − − − − − − {{reflist|refs= − − {{reflist|refs= − − {通货再膨胀 | 参考文献 − − <ref name="ldr11">{{Cite book |doi = 10.1007/978-3-642-29694-9_9|isbn = 978-3-642-29693-2|chapter = Recursively Generated Evolutionary Turing Machines and Evolutionary Automata |editor=Xin-She Yang |title = Artificial Intelligence, Evolutionary Computing and Metaheuristics|series = Studies in Computational Intelligence|year = 2013|last1 = Burgin|first1 = Mark|last2 = Eberbach|first2 = Eugene|volume = 427|pages = 201–230 |publisher=Springer-Verlag}}</ref> − − − − <ref name="ldr13">Burgin, M. and Eberbach, E. (2010) Bounded and Periodic Evolutionary Machines, in Proc. 2010 Congress on Evolutionary Computation (CEC'2010), Barcelona, Spain, 2010, pp. 1379-1386</ref> − − − − <ref name="ldr14">{{Cite journal |doi = 10.1093/comjnl/bxr099|title = Evolutionary Automata: Expressiveness and Convergence of Evolutionary Computation|year = 2012|last1 = Burgin|first1 = M.|last2 = Eberbach|first2 = E.|journal = The Computer Journal|volume = 55|issue = 9|pages = 1023–1029}}</ref> − − − − <ref name="ldr15">Eberbach E. (2002) On Expressiveness of Evolutionary Computation: Is EC Algorithmic?, Proc. 2002 World Congress on Computational Intelligence WCCI’2002, Honolulu, HI, 2002, 564-569.</ref> − − − − <ref name="ldr16">Eberbach, E. (2005) Toward a theory of evolutionary computation, BioSystems, v. 82, pp. 1-19.</ref> − − − − <ref name="ldr17">{{Cite book |doi = 10.1109/CEC.2009.4983207|isbn = 978-1-4244-2958-5|chapter = Evolutionary automata as foundation of evolutionary computation: Larry Fogel was right|title = 2009 IEEE Congress on Evolutionary Computation|year = 2009|last1 = Eberbach|first1 = Eugene|last2 = Burgin|first2 = Mark|pages = 2149–2156|publisher=IEEE}}</ref> − − − − <ref name="ldr18">Hopcroft, J.E., R. Motwani, and J.D. Ullman (2001) Introduction to Automata Theory, Languages, and Computation, Addison Wesley, Boston/San Francisco/New York</ref> − − − − }} − − }} − − }} − − − − − − − − − − <br />{{Evolutionary computation}} − − <br /> − − Br / − − − − − − [[Category:Evolutionary computation| ]] − − − − [[Category:Evolution]] − − Category:Evolution − − 分类: 进化
无编辑摘要
Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection. Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems. Genetic algorithms in particular became popular through the writing of John Holland. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.
Simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection. Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems. Genetic algorithms in particular became popular through the writing of John Holland. As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs. Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.
利用演化算法和人工生命模拟进化始于20世纪60年代 Nils Aall Barricelli的工作，后来被Alex Fraser扩展，他发表了一系列关于人工选择模拟的论文。20世纪60年代和70年代早期，Ingo Rechenberg 使用演化策略解决复杂的工程问题，人工演化因此成为被广泛认可的优化方法。尤其是遗传算法，因为[[约翰·霍兰德_John_H_Holland|约翰·霍兰德]]的著作而变得流行起来。学术界兴趣增长的同时，计算机能力的急剧增长使得这种算法可以被实际应用起来，其中包括计算机程序的自动演化。演化算法现在被用来解决多维度问题，且比人类设计者生产的软件更有效，同时还可以优化系统的设计。
利用演化算法和人工生命模拟进化始于20世纪60年代 Nils Aall Barricelli的工作，后来被Alex Fraser扩展，他发表了一系列关于人工选择模拟的论文<ref>{{cite journal |author=Fraser AS |title=Monte Carlo analyses of genetic models |journal=Nature |volume=181 |issue=4603 |pages=208–9 |year=1958 |pmid=13504138 |doi=10.1038/181208a0 |ref=harv|bibcode=1958Natur.181..208F }}</ref> 。20世纪60年代和70年代早期，Ingo Rechenberg 使用演化策略解决复杂的工程问题，人工演化因此成为被广泛认可的优化方法。尤其是遗传算法，因为[[约翰·霍兰德_John_H_Holland|约翰·霍兰德]]的著作而变得流行起来<ref>{{cite book |last=Holland |first=John H. |year=1975 |title=Adaptation in Natural and Artificial Systems |publisher=[[University of Michigan Press]] |isbn=978-0-262-58111-0 |url-access=registration |url=https://archive.org/details/adaptationinnatu00holl }}</ref>。学术界兴趣增长的同时，计算机能力的急剧增长使得这种算法可以被实际应用起来，其中包括计算机程序的自动演化。<ref>{{cite book |last=Koza|first=John R. |year=1992 |title=Genetic Programming: On the Programming of Computers by Means of Natural Selection|publisher=[[MIT Press]] |isbn=978-0-262-11170-6}}</ref> 演化算法现在被用来解决多维度问题，且比人类设计者生产的软件更有效，同时还可以优化系统的设计。 <ref>G. C. Onwubolu and B V Babu, {{cite book|url=https://www.springer.com/in/book/9783540201670|title=New Optimization Techniques in Engineering|accessdate=17 September 2016|isbn=9783540201670|last1=Onwubolu|first1=Godfrey C.|last2=Babu|first2=B. V.|date=2004-01-21}}</ref><ref>{{cite journal |author=Jamshidi M |title=Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms |journal=[[Philosophical Transactions of the Royal Society A]] |volume=361 |issue=1809 |pages=1781–808 |year=2003 |pmid=12952685 |doi=10.1098/rsta.2003.1225 |ref=harv|bibcode=2003RSPTA.361.1781J }}</ref>
演化算法是演化计算的一个子集，因为它们通常只涉及实现生物演化机制的技术，如繁殖、变异、重组、自然选择和适者生存。最佳化问题的候选解决方案扮演了人口中个体的角色，而成本函数决定了解决方案“生存”的环境(参见适应度函数)。在重复应用上述算子之后，种群的演化就发生了。
演化算法是演化计算的一个子集，因为它们通常只涉及实现生物演化机制的技术，如繁殖、变异、重组、自然选择和适者生存。最佳化问题的候选解决方案扮演了人口中个体的角色，而成本函数决定了解决方案“生存”的环境(参见适应度函数)。在重复应用上述算子之后，种群的演化就发生了。
在计算智能 computational intelligence 领域，演化算法（EA）（或称进化算法）是演化计算的子集 ，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<font color="#ff8000">损失函数 cost function（</font>决定了这些个体的生存环境（亦即用于计算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。
In this process, there are two main forces that form the basis of evolutionary systems: Recombination mutation and crossover create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.
In this process, there are two main forces that form the basis of evolutionary systems: Recombination mutation and crossover create the necessary diversity and thereby facilitate novelty, while selection acts as a force increasing quality.
在这个过程中，有两种主要的力量构成了演化系统的基础: 重组变异和交叉创造了必要的多样性，从而促进了新颖性，而选择作为一种力来提高质量。
在这个过程中，有两种主要的力量构成了演化系统的基础: 重组（Recombination）、突变（mutation）和交换（crossover）创造了必要的多样性，从而促进了新颖性，而选择带来的优胜劣汰则是一种提高质量的力量。
Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.
Many aspects of such an evolutionary process are stochastic. Changed pieces of information due to recombination and mutation are randomly chosen. On the other hand, selection operators can be either deterministic, or stochastic. In the latter case, individuals with a higher fitness have a higher chance to be selected than individuals with a lower fitness, but typically even the weak individuals have a chance to become a parent or to survive.
这种演化过程的许多方面都是随机的。由于重组和突变而改变的信息片段是随机选择的。另一方面，选择操作可以是确定性的，也可以是随机的。在后一种情况下，适合度较高的个体比适合度较低的个体有更高的机会被选中，但通常即使是适应度较差的个体也有机会成为父本或生存下来。
演化算法和生物学
== 演化算法和生物学 ==
{{Main|Evolutionary algorithm}}
{{Main|Evolutionary algorithm}}
[[Genetic algorithms]] deliver methods to model [[biological systems]] and [[systems biology]] that are linked to the theory of [[dynamical systems]], since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
[[Genetic algorithms]] deliver methods to model [[biological systems]] and [[systems biology]] that are linked to the theory of [[dynamical systems]], since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of development in biology.
遗传算法提供了与动力系统理论相关的生物系统和系统生物学模型的方法，因为它们可以被用来预测系统的未来状态。这是一种生动的(但也许是误导性的)方式，提醒人们注意生物学发展的有序、控制良好和高度结构化的特征。
This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers. Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.
This view has the merit of recognizing that there is no central control of development; organisms develop as a result of local interactions within and between cells. The most promising ideas about program-development parallels seem to us to be ones that point to an apparently close analogy between processes within cells, and the low-level operation of modern computers. Thus, biological systems are like computational machines that process input information to compute next states, such that biological systems are closer to a computation than classical dynamical system.
这一观点的优点是认识到发育没有中央控制；生物体的发育是细胞内部和细胞之间局部相互作用的结果。在我们看来，关于程序开发并行的最有前途的想法似乎是那些指出细胞内的进程与现代计算机的低级操作之间明显相似的思想。<ref>{{Cite book | chapter-url=https://plato.stanford.edu/entries/information-biological/#InfEvo | title=The Stanford Encyclopedia of Philosophy| chapter=Biological Information| publisher=Metaphysics Research Lab, Stanford University| year=2016}}</ref> 因此，生物系统就像计算机器，处理输入信息来计算下一个状态。这样看来，生物系统比经典的动力系统更接近于计算。<ref>{{cite journal |author= J.G. Diaz Ochoa |title= Elastic Multi-scale Mechanisms: Computation and Biological Evolution |journal=[[Journal of Molecular Evolution]] |volume=86 |issue=1 |pages=47–57 |year=2018 |pmid=29248946 |doi=10.1007/s00239-017-9823-7 |ref=harv|bibcode=2018JMolE..86...47D }}</ref>
Furthermore, following concepts from computational theory, micro processes in biological organisms are fundamentally incomplete and undecidable (completeness (logic)), implying that “there is more than a crude metaphor behind the analogy between cells and computers.
Furthermore, following concepts from computational theory, micro processes in biological organisms are fundamentally incomplete and undecidable (completeness (logic)), implying that “there is more than a crude metaphor behind the analogy between cells and computers.
此外，根据计算理论的概念，生物有机体中的微进程从根本上来说是不完整的和不可判定的(完整性(逻辑)) ，这意味着细胞和计算机之间的类比背后不只是一个粗略的比喻。
此外，根据计算理论的概念，生物有机体中的微进程从根本上来说是不完整的和不可判定的 ，这意味着细胞和计算机之间的类比不只仅仅只是一个粗略的比喻。
Evolutionary automata, a generalization of Evolutionary Turing machines, have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. Evolutionary finite automata, the simplest subclass of Evolutionary automata working in terminal mode can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine).
Evolutionary automata, a generalization of Evolutionary Turing machines, have been introduced in order to investigate more precisely properties of biological and evolutionary computation. In particular, they allow to obtain new results on expressiveness of evolutionary computation. This confirms the initial result about undecidability of natural evolution and evolutionary algorithms and processes. Evolutionary finite automata, the simplest subclass of Evolutionary automata working in terminal mode can accept arbitrary languages over a given alphabet, including non-recursively enumerable (e.g., diagonalization language) and recursively enumerable but not recursive languages (e.g., language of the universal Turing machine).
演化自动机是演化图灵机<font color="#ff8000"> 图灵机Turing machines</font>的一种推广，为了更精确地研究生物和演化计算的性质，人们引入了它。特别是，他们允许在演化计算的表现力上获得新的结果。这证实了关于自然演化和演化算法及过程不可判定性的初步结果。演化有限自动机是演化自动机中最简单的子类，在终端模式下可以接受给定字母表上的任意语言，包括非递归的可枚举语言(例如，对角化语言)和递归的可枚举但不递归语言(例如，通用图灵机语言)。
演化自动机是演化图灵机<font color="#ff8000"> 图灵机Turing machines</font>的一种推。人们引入了这一概念来更精确地研究生物和演化计算的性质。特别是，他们在演化计算的表现力上获得新的成果。这证实了关于自然演化和演化算法及过程不可判定性的初步结果。演化有限自动机是演化自动机中最简单的子类，在终端模式下可以接受给定字母表上的任意语言，包括非递归的可枚举语言(例如，对角化语言)和递归的可枚举但不递归语言(例如，通用图灵机语言)。
== Notable practitioners ==
== 著名的参与者 ==
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.<ref>{{cite arXiv |author=J.J. Merelo and C. Cotta |title=Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation |year=2007 |eprint=0708.2021|class=cs.CY }}</ref>
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.<ref>{{cite arXiv |author=J.J. Merelo and C. Cotta |title=Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation |year=2007 |eprint=0708.2021|class=cs.CY }}</ref>
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.
The list of active researchers is naturally dynamic and non-exhaustive. A network analysis of the community was published in 2007.
活跃的研究人员名单自然是动态的，并非详尽无遗。社区的网络分析在2007年发表。
活跃的研究人员名单自然是动态的，并非详尽无遗。社区的网络分析在2007年发表。<ref>{{cite arXiv |author=J.J. Merelo and C. Cotta |title=Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation |year=2007 |eprint=0708.2021|class=cs.CY }}</ref>
* [[Kalyanmoy Deb]]
* [[Kalyanmoy Deb]]
* [[Kenneth A De Jong]]
* [[Kenneth A De Jong]]
* [[Peter J. Fleming]]
* [[Peter J. Fleming]]
* [[David B. Fogel]]
* [[David B. Fogel]]
* [[Stephanie Forrest]]
* [[Stephanie Forrest]]
* [[David E. Goldberg]]
* [[David E. Goldberg]]
* [[John Henry Holland]]
* [[John Henry Holland]]
* [[Theo Jansen]]
* [[Theo Jansen]]
* [[John Koza]]
* [[John Koza]]
* [[Zbigniew Michalewicz]]
* [[Zbigniew Michalewicz]]
* [[Melanie Mitchell]]
* [[Melanie Mitchell]]
* [[Peter Nordin]]
* [[Peter Nordin]]
* [[Riccardo Poli]]
* [[Riccardo Poli]]
* [[Ingo Rechenberg]]
* [[Ingo Rechenberg]]
* [[Hans-Paul Schwefel]]
* [[Hans-Paul Schwefel]]
== 相关会议 ==
The main conferences in the evolutionary computation area include
The main conferences in the evolutionary computation area include
演化计算地区的主要会议包括
演化计算地区的主要会议包括
* [[Association for Computing Machinery|ACM]] [[Genetic and Evolutionary Computation Conference]] (GECCO),
* [[Association for Computing Machinery|ACM]] [[Genetic and Evolutionary Computation Conference]] (GECCO) 计算机协会 遗传与进化计算会议
* [[IEEE Congress on Evolutionary Computation]] (CEC) IEEE演化计算大会
* [[EvoStar]], which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART EvoStar，包括四个会议：EuroGP、EvoApplications、EvoCOP和EvoMUSART，
* Parallel Problem Solving from Nature (PPSN) 自然并行问题解决
* [[IEEE Congress on Evolutionary Computation]] (CEC),
IEEE演化计算大会
* [[EvoStar]], which comprises four conferences: EuroGP, EvoApplications, EvoCOP and EvoMUSART,
EvoStar，包括四个会议：EuroGP、EvoApplications、EvoCOP和EvoMUSART，
* Parallel Problem Solving from Nature (PPSN).
自然并行问题解决
== 更多 ==
* [[Adaptive dimensional search]] 适应性多维研究
* [[Artificial development]] 人工发展
* [[Autoconstructive]] 自动建设性
* [[Developmental biology]]发展性生物学
* [[Digital organism]]数字化器官
* [[Estimation of distribution algorithm]]分布算法估计
* [[Evolutionary robotics]]演化机器人
* [[Evolved antenna]]演化天线* [[Fitness approximation]]适应值近似
* [[Fitness function]]适应度函数
* [[Fitness landscape]]适应度局势
* [[Genetic operators]]遗传操作
* [[Grammatical evolution]]文法演化
* [[Human-based evolutionary computation]]人类演化计算
* [[Inferential programming]]推断程序设计
* [[Interactive evolutionary computation]]互动演化计算
* [[List of digital organism simulators]]数字化有机体模拟器表
* [[Mutation testing]]变异测试
* [[No free lunch in search and optimization]]研究和优化中的“没有免费午餐”
* [[Program synthesis]]程序综合
* [[Test functions for optimization]]优化测试函数
* [[Universal Darwinism]]普适达尔文主义
== 外部链接 ==
*[https://plato.stanford.edu/entries/information-biological/#InfEvo/ 斯坦福哲学百科：生物信息学]
== Bibliography ==
== 参考书目 ==
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* Th. Bäck, D.B. Fogel, and [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.amazon.com/Handbook-Evolutionary-Computation-Thomas-Back/dp/0750303921 Handbook of Evolutionary Computation], 1997, {{ISBN|0750303921}}
* Th. Bäck and H.-P. Schwefel. [http://caribou.iisg.agh.edu.pl/pub/svn/age/jage/legacy/papers/mgrKA/pdf/evco.1993.1.1.pdf An overview of evolutionary algorithms for parameter optimization]. Evolutionary Computation, 1(1):1–23, 1993.
* Th. Bäck and H.-P. Schwefel. [http://caribou.iisg.agh.edu.pl/pub/svn/age/jage/legacy/papers/mgrKA/pdf/evco.1993.1.1.pdf An overview of evolutionary algorithms for parameter optimization]. Evolutionary Computation, 1(1):1–23, 1993.
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* {{cite journal |authors=A. E. Eiben and M. Schoenauer |title=Evolutionary computing|journal=Information Processing Letters|volume=82|pages=1–6|doi=10.1016/S0020-0190(02)00204-1|year=2002}}
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* P. Hingston, L. Barone, and [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.springer.com/computer/ai/book/978-3-540-74109-1 Design by Evolution, Natural Computing Series], 2008, [[Springer Publishing|Springer]], {{ISBN|3540741097}}
* P. Hingston, L. Barone, and [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.springer.com/computer/ai/book/978-3-540-74109-1 Design by Evolution, Natural Computing Series], 2008, [[Springer Publishing|Springer]], {{ISBN|3540741097}}
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* F.J. Lobo, C.F. Lima, [[Zbigniew Michalewicz|Z. Michalewicz]] (Editors), [https://www.amazon.com/Parameter-Evolutionary-Algorithms-Computational-Intelligence/dp/3642088929/ Parameter Setting in Evolutionary Algorithms], [[Springer Publishing|Springer]], 2010, {{ISBN|3642088929}}
* [[Zbigniew Michalewicz|Z. Michalewicz]], [https://www.springer.com/computer/ai/book/978-3-540-60676-5 Genetic Algorithms + Data Structures – Evolution Programs], 1996, [[Springer Publishing|Springer]], {{ISBN|3540606769}}
* [[Zbigniew Michalewicz|Z. Michalewicz]], [https://www.springer.com/computer/ai/book/978-3-540-60676-5 Genetic Algorithms + Data Structures – Evolution Programs], 1996, [[Springer Publishing|Springer]], {{ISBN|3540606769}}
* [[Zbigniew Michalewicz|Z. Michalewicz]] and D.B. Fogel, [https://www.springer.com/computer/theoretical+computer+science/book/978-3-540-22494-5 How to Solve It: Modern Heuristics], [[Springer Publishing|Springer]], 2004, {{ISBN|978-3-540-22494-5}}
* [[Zbigniew Michalewicz|Z. Michalewicz]] and D.B. Fogel, [https://www.springer.com/computer/theoretical+computer+science/book/978-3-540-22494-5 How to Solve It: Modern Heuristics], [[Springer Publishing|Springer]], 2004, {{ISBN|978-3-540-22494-5}}
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* {{cite journal |authors=M. Sipper, W. Fu, K. Ahuja, and J. H. Moore |title=Investigating the parameter space of evolutionary algorithms|journal=BioData Mining|volume=11|pages=2|doi=10.1186/s13040-018-0164-x|pmid=29467825|pmc=5816380|year=2018}}
* {{cite journal |authors=M. Sipper, W. Fu, K. Ahuja, and J. H. Moore |title=Investigating the parameter space of evolutionary algorithms|journal=BioData Mining|volume=11|pages=2|doi=10.1186/s13040-018-0164-x|pmid=29467825|pmc=5816380|year=2018}}
* {{cite arxiv |authors=Y. Zhang and S. Li. |title=PSA: A novel optimization algorithm based on survival rules of porcellio scaber |eprint=1709.09840 |class=cs.NE |year=2017 }}
* {{cite arxiv |authors=Y. Zhang and S. Li. |title=PSA: A novel optimization algorithm based on survival rules of porcellio scaber |eprint=1709.09840 |class=cs.NE |year=2017 }}
== 引用 ==
{{reflist}}
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