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到目前为止，所列出的共同进化类型被描述为是两两运作的(也称为特定共同进化) ，其中一个物种的特征是直接响应第二个物种的特征而进化的，反之亦然。但事实并非总是如此。另一种进化模式出现在进化是相互的，但是是在一组物种之间而不是两个物种之间。这被称为公会或漫反射共同进化。例如，几种开花植物的一个特征，例如在长管的末端提供花蜜，可以与一种或几种传粉昆虫的特征共同进化，例如长喙。更一般地说，被子植物是由来自不同科的昆虫授粉的，包括蜜蜂、苍蝇和甲虫，所有这些昆虫形成了一个广泛的授粉者协会，它们对花朵产生的花蜜或花粉作出反应。朱恩格、托马斯和乔伊 · 贝尔格森。成对自然选择与扩散自然选择以及猩红吉利亚的多种食草动物——绿芽菜(Ipomopsis aggregata)进化(1998) : 1583-1592。
到目前为止，所列出的共同进化类型被描述为是两两运作的(也称为特定共同进化) ，其中一个物种的特征是直接响应第二个物种的特征而进化的，反之亦然。但事实并非总是如此。另一种进化模式出现在进化是相互的，但是是在一组物种之间而不是两个物种之间。这被称为公会或漫反射共同进化。例如，几种开花植物的一个特征，例如在长管的末端提供花蜜，可以与一种或几种传粉昆虫的特征共同进化，例如长喙。更一般地说，被子植物是由来自不同科的昆虫授粉的，包括蜜蜂、苍蝇和甲虫，所有这些昆虫形成了一个广泛的授粉者协会，它们对花朵产生的花蜜或花粉作出反应。朱恩格、托马斯和乔伊 · 贝尔格森。成对自然选择与扩散自然选择以及猩红吉利亚的多种食草动物——绿芽菜(Ipomopsis aggregata)进化(1998) : 1583-1592。
== Geographic mosaic theory ==
{{main|Mosaic coevolution}}
The geographic mosaic theory of coevolution was developed by [[John N. Thompson]] as a way of linking the ecological and evolutionary processes that shape interactions among species across ecosystems. It is based on three observations that are taken as assumptions: (1) species are usually groups of populations that are somewhat genetically distinct from each other, (2) interacting species often co-occur in only parts of their geographic ranges, and (3) interactions among species differ ecologically among environments.
The geographic mosaic theory of coevolution was developed by John N. Thompson as a way of linking the ecological and evolutionary processes that shape interactions among species across ecosystems. It is based on three observations that are taken as assumptions: (1) species are usually groups of populations that are somewhat genetically distinct from each other, (2) interacting species often co-occur in only parts of their geographic ranges, and (3) interactions among species differ ecologically among environments.
共同进化的地理镶嵌理论是由约翰 · n · 汤普森发展起来的，作为一种连接生态和进化过程的方式，塑造了生态系统中物种之间的相互作用。它是基于三个观察假设: (1)物种通常是群体的有些基因不同，(2)相互作用的物种经常共生在他们的地理范围的一部分，和(3)物种之间的相互作用在生态上不同的环境。
From these assumptions, geographic mosaic theory suggests that natural selection on interactions among species is driven by three sources of variation:
From these assumptions, geographic mosaic theory suggests that natural selection on interactions among species is driven by three sources of variation:
根据这些假设，地理镶嵌理论表明物种间相互作用的自然选择是由三个变异来源驱动的:
1. ''Geographic selection mosaics'' occur in interactions among species, because genes are expressed in different ways in different environments and because different genes are favored in different environments. For example, natural selection on an interaction between a parasite population and a host population may differ between very dry environments and very wet environments. Alternatively, an interaction between two or more species may be antagonistic in some environments but mutualistic (beneficial to both or all species) in other environments.
1. Geographic selection mosaics occur in interactions among species, because genes are expressed in different ways in different environments and because different genes are favored in different environments. For example, natural selection on an interaction between a parasite population and a host population may differ between very dry environments and very wet environments. Alternatively, an interaction between two or more species may be antagonistic in some environments but mutualistic (beneficial to both or all species) in other environments.
1.地理选择马赛克发生在物种之间的相互作用，因为基因在不同的环境中以不同的方式表达，因为不同的基因在不同的环境中受欢迎。例如，寄生虫种群和宿主种群相互作用的自然选择在非常干燥的环境和非常湿润的环境之间可能有所不同。或者，两个或两个以上物种之间的相互作用在某些环境中可能是对抗性的，但在其他环境中是互惠性的(对两个或所有物种都有益)。
2. ''Coevolutionary hotspots and coldspots'' occur because natural selection on interactions among species is reciprocal in some environments but not in others. For example, a symbiont population may decrease the survival or reproduction of its hosts in one environment, but it may have no effect on host survival or reproduction in another environment. When detrimental, natural selection will favor evolutionary responses in the host population, resulting in a coevolutionary hotspot of ongoing reciprocal evolutionary changes in the parasite and host populations. When the symbiont has no effect on the survival and reproduction of the host, natural selection on the symbiont population will not favor an evolutionary response by the host population (i.e, a coevolutionary coldspot).
2. Coevolutionary hotspots and coldspots occur because natural selection on interactions among species is reciprocal in some environments but not in others. For example, a symbiont population may decrease the survival or reproduction of its hosts in one environment, but it may have no effect on host survival or reproduction in another environment. When detrimental, natural selection will favor evolutionary responses in the host population, resulting in a coevolutionary hotspot of ongoing reciprocal evolutionary changes in the parasite and host populations. When the symbiont has no effect on the survival and reproduction of the host, natural selection on the symbiont population will not favor an evolutionary response by the host population (i.e, a coevolutionary coldspot).
2.共同进化热点和冷点的出现是因为物种间相互作用的自然选择在某些环境中是相互的，而在其他环境中则不然。例如，一个共生生物种群可能会减少其宿主在一个环境中的生存或繁殖，但它可能对宿主在另一个环境中的生存或繁殖没有影响。当有害时，自然选择将有利于宿主种群的进化反应，从而导致寄生虫和宿主种群中正在进行的相互进化变化的共同进化热点。当共生生物对宿主的生存和繁殖没有影响时，共生生物种群的自然选择不利于宿主种群的进化反应(即共同进化的冷斑)。
3. Finally, there is constant ''remixing of the traits'' on which natural selection acts both locally and regionally. At any moment in time, a local population will have a unique combination of genes on which natural selection acts. These genetic differences among populations occur because each local population has a unique history of new mutations, genomic alterations (e.g., whole genome duplications), gene flow among populations from individuals arriving from other populations or going to other populations, random loss or fixation of genes at times when populations are small (random genetic drift), hybridization with other species, and other genetic and ecological processes that affect the raw genetic material on which natural selection acts. More formally, then, the geographic mosaic of coevolution can be viewed as a genotype by genotype by environment interaction (GxGxE) that results in the relentless evolution of interacting species.
3. Finally, there is constant remixing of the traits on which natural selection acts both locally and regionally. At any moment in time, a local population will have a unique combination of genes on which natural selection acts. These genetic differences among populations occur because each local population has a unique history of new mutations, genomic alterations (e.g., whole genome duplications), gene flow among populations from individuals arriving from other populations or going to other populations, random loss or fixation of genes at times when populations are small (random genetic drift), hybridization with other species, and other genetic and ecological processes that affect the raw genetic material on which natural selection acts. More formally, then, the geographic mosaic of coevolution can be viewed as a genotype by genotype by environment interaction (GxGxE) that results in the relentless evolution of interacting species.
3.最后，自然选择在局部和区域两方面作用的特征不断重新混合。在任何时候，当地的种群都会有一个独特的基因组合，自然选择对其起作用。这些种群之间的遗传差异之所以会出现，是因为每个当地种群都有新突变、基因组改变(例如全基因组复制)、来自其他种群或前往其他种群的个体的种群之间的基因流动、在种群较小时随机丢失或固定基因(随机遗传漂变)、与其他种群杂交，以及影响自然选择作用的原始遗传物质的其他遗传和生态过程。更正式地说，共同进化的地理拼图可以被看作是一种基因型与环境相互作用(GxGxE) ，这种作用导致了相互作用物种的无情进化。
Geographic mosaic theory has been explored through a wide range of mathematical models, studies of interacting species in nature, and laboratory experiments using microbial species and viruses.<ref name="Thompson, John N. 2005"/><ref name="Thompson, John N"/>
Geographic mosaic theory has been explored through a wide range of mathematical models, studies of interacting species in nature, and laboratory experiments using microbial species and viruses.
地理镶嵌理论已经通过广泛的数学模型，研究自然界中相互作用的物种，以及使用微生物物种和病毒的实验室实验得到探索。
==Outside biology==
Coevolution is primarily a biological concept, but has been applied to other fields by analogy.
Coevolution is primarily a biological concept, but has been applied to other fields by analogy.
共同进化主要是一个生物学概念，但已类推应用于其他领域。
===In algorithms===
{{See also|Evolutionary computation}}
Coevolutionary algorithms are used for generating [[artificial life]] as well as for optimization, game learning and [[machine learning]].<ref>Potter M. and K. De Jong, Evolving Complex Structures via Cooperative Coevolution, Fourth Annual Conference on Evolutionary Programming, San Diego, CA, 1995.</ref><ref>Potter M., The Design and Computational Model of Cooperative Coevolution, PhD thesis, George Mason University, Fairfax, Virginia, 1997.</ref><ref>{{cite journal|last1=Potter|first1=Mitchell A.|last2=De Jong|first2=Kenneth A.|title=Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents|journal=Evolutionary Computation|date=2000|volume=8|issue=1|pages=1–29|doi=10.1162/106365600568086|pmid=10753229|citeseerx=10.1.1.134.2926|s2cid=10265380}}</ref><ref>Weigand P., Liles W., De Jong K., An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2001.</ref><ref>Weigand P., An Analysis of Cooperative Coevolutionary Algorithms, PhD thesis, George Mason University, Fairfax, Virginia, 2003.</ref> [[Daniel Hillis]] added "co-evolving parasites" to prevent an optimization procedure from becoming stuck at local maxima.<ref>{{citation |author=Hillis, W.D. |year=1990 |title=Co-evolving parasites improve simulated evolution as an optimization procedure |journal=Physica D: Nonlinear Phenomena |volume=42 |issue=1–3 |pages=228–234 |doi=10.1016/0167-2789(90)90076-2|bibcode=1990PhyD...42..228H}}</ref> [[Karl Sims]] coevolved virtual creatures.<ref>{{cite web|last1=Sims |first1=Karl |title=Evolved Virtual Creatures |url=http://www.karlsims.com/evolved-virtual-creatures.html|publisher=Karl Sims|access-date=17 January 2017|date=1994}}</ref>
Coevolutionary algorithms are used for generating artificial life as well as for optimization, game learning and machine learning.Potter M. and K. De Jong, Evolving Complex Structures via Cooperative Coevolution, Fourth Annual Conference on Evolutionary Programming, San Diego, CA, 1995.Potter M., The Design and Computational Model of Cooperative Coevolution, PhD thesis, George Mason University, Fairfax, Virginia, 1997.Weigand P., Liles W., De Jong K., An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2001.Weigand P., An Analysis of Cooperative Coevolutionary Algorithms, PhD thesis, George Mason University, Fairfax, Virginia, 2003. Daniel Hillis added "co-evolving parasites" to prevent an optimization procedure from becoming stuck at local maxima. Karl Sims coevolved virtual creatures.
协同进化算法用于生成人工生命，以及优化，博弈学习和机器学习。和 k. De Jong，通过合作共同进化进化复杂结构，第四届进化规划年会，圣地亚哥，加利福尼亚州，1995。合作共同进化的设计与计算模型，博士论文，乔治梅森大学，费尔法克斯，弗吉尼亚州，1997。合作共同进化算法中协作方法的实证分析。2001年遗传学和进化计算学会会议论文集。合作共同进化算法分析》 ，博士论文，乔治梅森大学，弗吉尼亚州费尔法克斯，2003年。Daniel Hillis 补充了“共同进化寄生虫”，以防止优化过程陷入局部极大值。卡尔 · 西姆斯共同进化了虚拟生物。