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organisms instead of parasitic threads. For example, one way to set up a plant-pollinator type of interaction is to use an environment containing two mutually exclusive resources: one designated for "plant" organisms and one for "pollinator" organisms. Similar to parasites attempting infection, if tasks overlap between a pollinator and a plant it visits, pollination is successful and both organisms obtain extra CPU cycles. Thus, these digital organisms obtain mutual benefit when they perform at least one common task, and more common tasks lead to larger mutual benefits. While this is one specific way to enable mutualistic interactions, many others are possible in Avida. Interactions that begin as parasitic may even evolve to be mutualistic under the right conditions. In most cases, coevolution will result in concurrent interactions between multiple phenotypes. Thus, observed networks of mutualistic interactions can inform our understanding about the outcomes and processes of coevolution in complex communities.
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coevolution shapes the emergence and diversification of coevolving species interaction networks and, in turn, how changes in the overall structure of the web (e.g., through extinction of taxa or the introduction of invasive species) affect the evolution of a given species. Studying the evolution of species interaction networks in these artificial evolving systems also contributes to the development of the field, while overcoming limitations evolutionary biologists may face. For example, laboratory studies have shown that historical contingency can enable or impede the outcome of the interactions between bacteria and phage, depending on the order in which mutations occur: the phage often, but not always, evolve the ability to infect a novel host. Therefore, in order to obtain
168:. The concept of diffuse coevolution, where adaptation is in response to a suite of biotic interactions, was the first step towards a framework unifying relevant theories in community ecology and coevolution. Understanding how individual interactions within networks influence coevolution, and conversely how coevolution influences the overall structure of networks, requires an appreciation for how pair-wise interactions change due to their broader community contexts as well as how this community context shapes selective pressures. Accordingly, research is now focusing on how reciprocal selection influences and is embedded within the structure of multispecies interactive webs, not only on particular species in isolation.
649:. A key caveat in translating predictions of evolving digital networks to biological ones is that mechanistic details may differ substantially between interacting digital organisms and interacting biological organisms. Nevertheless, the general operational processes (Darwinian evolution, mutualism, parasitism, etc.) are equivalent, and so studies utilizing digital networks can uncover rules shaping the web of interactions among species and their coevolutionary processes. The evolution of digital communities and their ecological networks also allows for a perfect '
508:(red) phenotypes are represented as columns (on the top) and as circles (below). On the top, each column depicts a phenotype and each row represents a task. Tasks performed by each phenotype are filled. In the lower part, the interaction networks between hosts and parasites are illustrated, which result from phenotypic matching: a parasite infects a host (indicated by a line) if it performs at least one task that is also performed by the host. Inset numbers indicate the identity of phenotypes represented on the top. Arrows represent the temporal direction of the
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375:) is used to move the other pointers to genetically specified locations. The remainder of the process of self replication is carried out by a set of instructions at the end of the genome, commonly referred to as the copy-loop. When execution reaches the copy-loop, the flow pointer is used to keep the flow of execution inside of a loop that advances the read and write heads and copies instructions from the parent genome (read-head) to the offspring genome (write-head). Arcs inside the circular genome represent the
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187:. In spite of the long time scales involved and the substantial effort that is necessary to isolate and quantify samples, the latter approach of testing biological evolution in the lab has been successful over the last two decades. However, studying the evolution of interspecific interactions, which involves dealing with more complex webs of multiple interacting species, has proven to be a much more difficult challenge. A meta-analysis of
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abundance of individuals expressing each phenotype changes continuously (indicated by node size) altering interaction patterns, and thus influencing subsequent coevolutionary dynamics. Interactions between a host phenotype and a parasite phenotype are depicted as arrows pointing in opposite directions: the thickness of red arrows indicates the fraction of
290:, which were originally referred to as parasites in Ray's work, to further shrink their genetic programs. This form of cheating was the first evolved ecological interaction between organisms in artificial life software. Ray's cheaters pre-dated the formal study of evolving ecological interactions using Tierra-like digital evolution platforms by 20 years.
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thicknesses of arrow-pairs leads to red arrows dominating the picture. At these times, most parasite phenotypes are infecting only a small fraction of hosts expressing a given phenotype. Instead, the majority of those hosts are being infected by parasites with other phenotypes. This animation was generated using Pajek, which is available under the
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562:, researchers are able to perfectly reconstruct the interaction networks of digital coevolving hosts and parasites. The structure of these networks is a result of the interplay between ecological processes, mainly host abundance, and coevolutionary dynamics, which lead to changes in host specificity.
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Interactions between digital organisms occur through phenotypic matching, which, in the case of task-based phenotypes, results from the performance of overlapping logic functions. Different mechanisms for mapping phenotypic matching to interactions can be implemented, depending on the antagonistic or
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favors behavioral strategies in prey that enable them to avoid being eaten. At the same time, selection favors predators with behavioral strategies that improve their food finding and prey attacking abilities. The resulting diversity in the continuously evolving behavioral phenotypes creates dynamic
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In traditional infection genetic models, host resistance and pathogen infectivity have associated costs. These costs are an important part of theory about why defense genes do not always fix rapidly within populations. Costs are also present in digital host-parasite interactions: performing more or
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The circular genome of a digital organism, on the left, consists of a set of instructions (represented here as letters). Some of these instructions are involved in the copy process and others in completing computational tasks. The experimenter determines the probability of mutations. Copy mutations
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interactions are determined by behavior. Predators are digital organisms that have evolved from ancestral prey phenotypes to locate, attack, and consume organisms. When a predator executes an attack instruction (acquired through mutation), it kills a neighboring organism. When predators kill prey,
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The inclusion of ecological interactions in digital systems enables new research avenues: investigations using self-replicating computer programs complement laboratory efforts by broadening the breadth of viable experiments focused on the emergence and diversification of coevolving interactions in
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Interactions in which both species obtain mutual benefit, such as those between flowering plants and pollinators, and birds and fleshy fruits, can be implemented in evolving digital experiments by following the same task matching approach used for host-parasite interactions, but using free-living
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Digital ecological networks enable the direct, comprehensive, and real time observation of evolving ecological interactions between antagonistic and/or mutualistic digital organisms that are difficult to study in nature. Research using self-replicating computer programs can help us understand how
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interaction networks, carried out by Weitz and his team, found a striking statistical structure to the patterns of infection and resistance across a wide variety of environments and methods from which the hosts and phage were obtained. However, the ecological mechanisms and evolutionary processes
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for performing computational tasks, like adding two numbers together. In Avida, researchers can define the available tasks and set the consequences for organisms upon successful calculation. When organisms are rewarded with additional CPU cycles, their replication rate increases. Since Avida was
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For example, the stability-diversity debate is a long-standing debate about whether more diverse ecological networks are also more stable. Mathematical models were able to show that a mixture of antagonistic and mutualistic interactions can stabilize population dynamics and that the loss of one
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that a particular parasite is responsible for inflicting on the indicated host phenotype, while the thickness of the green arrows indicates the fraction of all of the hosts a particular parasite phenotype infects that is accounted for by the indicated host phenotype. Often asymmetry between the
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Starting from a host phenotype (green node) and a parasite phenotype (red node), a complex network of interactions (arrows) between hosts and parasites emerges from the coevolutionary process. Nodes representing new host and parasite phenotypes appear and disappear over evolutionary time. The
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occurs when both the parasite and host perform at least one overlapping task. Thus a host is resistant to a particular parasite if they do not share any tasks. This mechanism of infection mimics the inverse-gene-for-gene model, in which infection only occurs if a host susceptibility gene (the
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predator-prey interaction networks in which selective forces are constantly changing as a consequence of the emergence of new, and loss of old, behaviors. Because predators and prey move around in and use information about their environment, these experiments are typically carried out using
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that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within
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512:: from the earliest phenotype to the most recent one. The order of tasks (from top to bottom) indicates the time needed for a digital organism to perform that task over the course of the evolutionary trajectory. Depending on the pattern of tasks performed by the digital organisms, a
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designed specifically as a scientific tool, it allows users to collect a comprehensive suite of data about evolving populations. Due to its flexibility and data tracking abilities, Avida has become the most widely used digital system for studying evolution. The
Devolab at the
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using CPU cycles stolen from their hosts. Because parasites impose a cost (lost CPU cycles) on hosts, there is selection for resistance, and when resistance starts to spread in a population, there is selective pressure for parasites to infect those new resistant hosts.
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more complex tasks implies larger genomes and hence slower reproduction. Tasks may also allow organisms access to resources present in the abiotic environment, and the environment can be carefully manipulated to control the relative costs or benefits of resistance.
457:. With only this instruction, digital organisms can compute any other task by stringing together various operations because NAND is a universal logic function. If the output of processing random numbers from the environment corresponds to the result of a particular
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in Tierra was for rapid self-replication. Over the course of evolution, this pressure led to shorter and shorter genomes, reducing the time spent copying instructions during replication. Some individuals even started executing the replication
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Zaman, L., Devangam, S, Ofria, C. (2011). Rapid host-parasite coevolution drives the production and maintenance of diversity in digital organisms. Proceedings of the 13th Annual
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216:. Such experiments allow researchers to quantify the role of historical contingency and repeatability in network evolution, enabling predictions about the architecture and dynamics of large networks of interacting species.
653:' of how the number and patterns of links among interacting phenotypes evolved. The selection pressures and parameters can also be controlled to an extent that is impossible in experimental evolution of living organisms.
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being used during the copying process. After genome replication is complete, the parent organism divides off its offspring, which must now fend for itself within the Avida world. This animation was generated using
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occur when an instruction is copied incorrectly, and is instead replaced by a random instruction in the forming offspring's genome (as can be seen in the offspring, on the right). Other types of mutations, such as
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In nature, species do not evolve in isolation but in large networks of interacting species. One of the main goals in evolutionary ecology is to disentangle the evolutionary mechanisms that shape and are shaped by
496:, the digital organism is said to have performed that task. The combination of tasks performed by a digital organism partially defines its phenotype. The center of the figure depicts the output of applying eight
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Digital organisms in Avida are self-replicating computer programs with a genome composed of assembly-like instructions. The genetic programming language in Avida contains instructions for manipulating values in
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629:. On the other hand, host-parasite and mutualistic coevolution are often done in well-mixed environments, though the choice of the environment is at the discretion of the experimenter.
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533:, the parasite organisms benefits at the expense of the host organisms. Parasites in Avida are implemented just like other self-replicating digital organisms, but they live inside
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presence of a logic task) is matched by a parasite virulence gene (a parasite performing the same task). Additional infection mechanisms, such as the matching allele and
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game, where programs competed in a digital battle ground for the computer's resources. Although
Rasmussen observed some interesting evolution, mutations in this early
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complex communities. This cross-disciplinary research program provides fertile grounds for new collaborations between computer scientists and evolutionary biologists.
114:). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological
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on which its genome is executed. To reproduce, digital organisms must copy their genome instruction by instruction into a new region of
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of the 1980s. These viruses were self-replicating computer programs that spread from one computer to another, but they did not evolve.
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they gain resources required for reproduction (e.g., CPU cycles) proportional to the level accumulated by the consumed prey.
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Logical computations, i.e. tasks, partially define phenotypes, and phenotypic matching leads to ecological interactions
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from the environment and are able to manipulate them using their genetic instructions, including the logic instruction
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64:. However, because species coevolve within large networks of multispecies ecological interactions, this example of
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for predicting such outcomes of the coevolutionary process, experiments require a high level of replication. This
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461:, then that task is incorporated into the set of tasks the organism performs, which in turn, defines part of its
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rubbed onto its head, and the next orchid visited would then be pollinated. In 1903, such a moth was discovered:
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By keeping track of task-based phenotypes as well as tracking information about successful infections in the
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via ecological patterns that persist and are observable today, and by performing laboratory experiments with
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Studies of digital organisms allows a depth of research of evolutionary processes that is not possible with
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interaction type may critically destabilize ecosystems. These techniques also enable detailed analysis of
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language produced many unstable organisms, thus prohibiting scientific experiments. Just one year later,
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1343:"Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations"
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586:
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412:
212:, evolving digital ecological networks open the door to experiments that incorporate this approach of
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can be observed in real-time by tracking interactions between the constantly evolving organism
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will occasionally have higher fitness than their parents, thereby providing the basis for
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long enough to reach the nectar. In its attempt to get the nectar, the moth would have
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nature of the evolutionary process was exemplified by
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Meyer JR, Dobias DT, Weitz JS, Barrick JE, Quick RT, Lenski RE (January 2012).
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Proceedings of the
National Academy of Sciences of the United States of America
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Proceedings of the
National Academy of Sciences of the United States of America
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What would happen if the tape of the history of life were rewound and replayed?
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system, Tierra. Primarily, he prevented instructions from writing beyond the
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taken from the environment using the instructions that constitute their
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are also implemented. Initially, all three of the parent's hardware
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are in the same location, at the instruction represented here by
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359:. As execution begins, the instruction pointer (indicated by an
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between species. A particularly important question concerns how
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Madagascan Hawk Moth prediction".
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489:
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314:. They added the ability for digital organisms to obtain bonus
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was one and a half feet long, he surmised that there must be a
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Knowledge articles published in peer-reviewed literature (J2W)
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363:) advances. The first few instructions allocate space for the
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and mathematical operations. Each digital organism contains
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Thompson JN (February 2009). "The coevolving web of life".
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This article was adapted from the following source under a
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Knowledge articles published in PLOS Computational Biology
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306:, and C. Titus Brown created the artificial life platform
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Knowledge articles published in peer-reviewed literature
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interactions are determined by task-based phenotypes,
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The field of digital life was inspired by the rampant
371:) into that space. The flow pointer (indicated by an
122:. These phenotypes may be defined by combinations of
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and simulation, by looking at ancient footprints of
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Coevolution in a community context can be addressed
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1402:"Statistical structure of host-phage interactions"
1292:
2040:
1991:
1336:
1301:
699:Miguel A Fortuna; Luis Zaman; Aaron P Wagner;
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602:
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388:, which is available under the terms of the
208:") Because of their ease in scalability and
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323:currently continues development of Avida.
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443:with all of the necessary components for
435:). While most mutations are detrimental,
60:. This was an example of an evolutionary
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520:(right) interaction network can emerge.
474:mutualistic nature of the interaction.
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447:. Digital organisms can acquire random
338:Self-replication of a digital organism.
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705:"Evolving digital ecological networks"
312:the California Institute of Technology
192:responsible have yet to be unraveled.
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73:Evolving digital ecological networks
68:is more the exception than the rule.
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1024:
801:Post DM, Palkovacs EP (June 2009).
286:in other organisms, allowing those
156:, is shaped by the architecture of
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551:models, can also be implemented.
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390:GNU Lesser General Public License
253:developed an alternative system,
90:) that experience the same major
1859:10.1111/j.1461-0248.2011.01649.x
1324:10.1046/j.1461-0248.2000.00161.x
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1769:Evolutionary Ecology Research
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1539:10.1016/0167-2789(90)90070-6
724:10.1371/JOURNAL.PCBI.1002928
577:Evolving host-parasite webs.
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1623:10.1162/106454604773563612
1070:The coevolutionary process
710:PLOS Computational Biology
603:Predator-prey interactions
587:GNU General Public License
531:host-parasite interactions
525:Host-parasite interactions
484:Digital organisms process
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214:replaying the tape of life
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166:host-parasite communities
75:are webs of interacting,
1800:10.1016/j.pt.2010.05.008
594:Mutualistic interactions
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2014:10.1126/science.1188321
1963:10.1126/science.1220529
1726:The American Naturalist
1477:10.1126/science.1214449
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1368:10.1073/pnas.91.15.6808
1085:The American Naturalist
918:10.1126/science.1193954
855:The American Naturalist
659:coevolutionary dynamics
146:patterns of interaction
92:ecological interactions
1788:Trends in Parasitology
819:10.1098/rstb.2009.0012
663:historical contingency
647:experimental evolution
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537:and execute parasitic
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510:coevolutionary process
427:through a potentially
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379:, showing most of the
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776:American Entomologist
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177:mathematical modeling
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2074:Evolutionary biology
1068:Thompson JN (1994).
469:Digital interactions
181:evolutionary history
162:mutualistic networks
133:evolutionary ecology
124:logical computations
96:biological organisms
66:pairwise coevolution
21:Pairwise coevolution
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1955:2012Sci...337..349M
1851:2011EcolL..14..877G
1821:Pajek Wiki Homepage
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1573:Avida download page
1531:1990PhyD...42..111R
1469:2012Sci...335..428M
1359:1994PNAS...91.6808L
1316:2000EcolL...3..362B
1260:10.1038/nature09113
1252:2010Natur.465..918G
1199:10.1038/nature05956
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910:2011Sci...331..426S
788:10.1093/ae/37.4.206
667:genetic constraints
445:Darwinian evolution
247:genetic programming
1578:2012-07-29 at the
639:natural ecosystems
591:
522:
394:
279:selective pressure
70:
57:Xanthopan morganii
813:(1523): 1629–40.
573:
498:logical operators
441:natural selection
402:Digital organisms
334:
198:statistical power
154:natural selection
88:digital organisms
84:computer programs
2086:
2034:
2033:
1989:
1983:
1982:
1949:(6092): 349–51.
1938:
1932:
1931:
1912:10.1038/35012234
1906:(6783): 228–33.
1895:
1889:
1888:
1870:
1834:
1828:
1818:
1812:
1811:
1783:
1777:
1776:
1764:
1758:
1757:
1721:
1715:
1711:
1705:
1704:
1660:
1654:
1649:
1643:
1642:
1616:
1596:
1587:
1570:
1561:
1560:
1552:
1543:
1542:
1514:
1508:
1505:
1499:
1498:
1488:
1463:(6067): 428–32.
1448:
1442:
1441:
1431:
1421:
1397:
1391:
1390:
1380:
1370:
1334:
1328:
1327:
1299:
1290:
1289:
1271:
1246:(7300): 918–21.
1235:
1229:
1228:
1210:
1174:
1168:
1167:
1134:(7389): 328–30.
1123:
1117:
1116:
1080:
1074:
1073:
1065:
1059:
1058:
1022:
1016:
1015:
979:
973:
972:
955:(5423): 2116–8.
944:
938:
937:
893:
887:
886:
850:
841:
840:
830:
798:
792:
791:
771:
763:
762:
744:
726:
695:reviewer reports
688:
681:
574:
421:virtual hardware
335:
235:computer viruses
77:self-replicating
2094:
2093:
2089:
2088:
2087:
2085:
2084:
2083:
2069:Artificial life
2039:
2038:
2037:
2000:(5993): 853–6.
1990:
1986:
1939:
1935:
1896:
1892:
1839:Ecology Letters
1835:
1831:
1819:
1815:
1784:
1780:
1765:
1761:
1722:
1718:
1712:
1708:
1671:(6745): 661–4.
1661:
1657:
1650:
1646:
1614:10.1.1.211.8981
1601:Artificial Life
1597:
1590:
1584:WebCite archive
1580:Wayback Machine
1571:
1564:
1553:
1546:
1525:(1–3): 111–34.
1515:
1511:
1506:
1502:
1449:
1445:
1412:(28): E288–97.
1398:
1394:
1353:(15): 6808–14.
1335:
1331:
1304:Ecology Letters
1300:
1293:
1236:
1232:
1185:(7156): 925–8.
1175:
1171:
1124:
1120:
1081:
1077:
1066:
1062:
1047:10.2307/1941243
1023:
1019:
996:10.2307/2408229
980:
976:
945:
941:
904:(6016): 426–9.
894:
890:
851:
844:
799:
795:
772:
768:
717:(3): e1002928.
698:
684:
682:
675:
635:
605:
596:
565:
527:
471:
415:as well as for
404:
399:
326:
300:Christoph Adami
296:
267:artificial life
263:
239:Steen Rasmussen
231:
226:
160:, plant-animal
141:
12:
11:
5:
2092:
2082:
2081:
2076:
2071:
2066:
2061:
2056:
2051:
2036:
2035:
1984:
1933:
1890:
1829:
1813:
1778:
1759:
1738:10.1086/645087
1732:(6): E230–42.
1716:
1706:
1655:
1644:
1607:(2): 191–229.
1588:
1562:
1544:
1509:
1500:
1443:
1392:
1329:
1310:(4): 362–377.
1291:
1230:
1169:
1118:
1097:10.1086/657048
1075:
1060:
1033:(4): 906–907.
1017:
990:(3): 611–612.
974:
939:
888:
867:10.1086/595752
842:
793:
782:(4): 206–209.
765:
674:
671:
634:
631:
604:
601:
595:
592:
526:
523:
494:logic function
486:binary numbers
470:
467:
449:binary numbers
403:
400:
398:
397:Implementation
395:
377:execution flow
295:
292:
262:
259:
230:
227:
225:
222:
185:microorganisms
140:
137:
9:
6:
4:
3:
2:
2091:
2080:
2077:
2075:
2072:
2070:
2067:
2065:
2062:
2060:
2057:
2055:
2052:
2050:
2047:
2046:
2044:
2031:
2027:
2023:
2019:
2015:
2011:
2007:
2003:
1999:
1995:
1988:
1980:
1976:
1972:
1968:
1964:
1960:
1956:
1952:
1948:
1944:
1937:
1929:
1925:
1921:
1917:
1913:
1909:
1905:
1901:
1894:
1886:
1882:
1878:
1874:
1869:
1864:
1860:
1856:
1852:
1848:
1845:(9): 877–85.
1844:
1840:
1833:
1826:
1825:WebCite copy)
1822:
1817:
1809:
1805:
1801:
1797:
1794:(10): 492–8.
1793:
1789:
1782:
1774:
1770:
1763:
1755:
1751:
1747:
1743:
1739:
1735:
1731:
1727:
1720:
1710:
1702:
1698:
1694:
1690:
1686:
1685:10.1038/23245
1682:
1678:
1674:
1670:
1666:
1659:
1653:
1648:
1640:
1636:
1632:
1628:
1624:
1620:
1615:
1610:
1606:
1602:
1595:
1593:
1585:
1581:
1577:
1574:
1569:
1567:
1558:
1551:
1549:
1540:
1536:
1532:
1528:
1524:
1520:
1513:
1504:
1496:
1492:
1487:
1482:
1478:
1474:
1470:
1466:
1462:
1458:
1454:
1447:
1439:
1435:
1430:
1425:
1420:
1415:
1411:
1407:
1403:
1396:
1388:
1384:
1379:
1374:
1369:
1364:
1360:
1356:
1352:
1348:
1344:
1341:(July 1994).
1340:
1333:
1325:
1321:
1317:
1313:
1309:
1305:
1298:
1296:
1287:
1283:
1279:
1275:
1270:
1265:
1261:
1257:
1253:
1249:
1245:
1241:
1234:
1226:
1222:
1218:
1214:
1209:
1204:
1200:
1196:
1192:
1188:
1184:
1180:
1173:
1165:
1161:
1157:
1153:
1149:
1145:
1141:
1137:
1133:
1129:
1122:
1114:
1110:
1106:
1102:
1098:
1094:
1091:(6): 802–17.
1090:
1086:
1079:
1071:
1064:
1056:
1052:
1048:
1044:
1040:
1036:
1032:
1028:
1021:
1013:
1009:
1005:
1001:
997:
993:
989:
985:
978:
970:
966:
962:
958:
954:
950:
943:
935:
931:
927:
923:
919:
915:
911:
907:
903:
899:
892:
884:
880:
876:
872:
868:
864:
861:(2): 125–40.
860:
856:
849:
847:
838:
834:
829:
824:
820:
816:
812:
808:
804:
797:
789:
785:
781:
777:
770:
766:
764:
760:
756:
752:
748:
743:
738:
734:
730:
725:
720:
716:
712:
711:
706:
702:
701:Charles Ofria
696:
692:
687:
680:
670:
668:
664:
660:
654:
652:
651:fossil record
648:
644:
640:
630:
628:
623:
618:
617:predator-prey
614:
610:
609:host-parasite
600:
588:
583:
578:
563:
561:
556:
552:
550:
549:gene-for-gene
545:
540:
536:
532:
519:
515:
511:
507:
503:
499:
495:
491:
487:
483:
479:
475:
466:
464:
460:
456:
455:
450:
446:
442:
438:
434:
430:
429:noisy channel
426:
422:
418:
414:
410:
391:
387:
382:
378:
374:
370:
366:
362:
358:
354:
353:
348:
344:
339:
324:
322:
321:BEACON Center
317:
313:
309:
305:
304:Charles Ofria
301:
291:
289:
285:
280:
276:
272:
268:
258:
256:
252:
251:Thomas S. Ray
248:
244:
240:
236:
221:
217:
215:
211:
207:
203:
199:
193:
190:
186:
182:
178:
174:
173:theoretically
169:
167:
163:
159:
155:
151:
147:
136:
134:
129:
125:
121:
117:
113:
109:
105:
101:
97:
93:
89:
85:
82:
78:
74:
67:
63:
59:
58:
53:
49:
45:
42:
38:
34:
30:
26:
22:
18:
1997:
1993:
1987:
1946:
1942:
1936:
1903:
1899:
1893:
1842:
1838:
1832:
1816:
1791:
1787:
1781:
1772:
1768:
1762:
1729:
1725:
1719:
1709:
1668:
1664:
1658:
1647:
1604:
1600:
1556:
1522:
1518:
1512:
1503:
1460:
1456:
1446:
1409:
1405:
1395:
1350:
1346:
1332:
1307:
1303:
1243:
1239:
1233:
1182:
1178:
1172:
1131:
1127:
1121:
1088:
1084:
1078:
1069:
1063:
1030:
1026:
1020:
987:
983:
977:
952:
948:
942:
901:
897:
891:
858:
854:
810:
806:
796:
779:
775:
769:
714:
708:
676:
665:, and their
655:
636:
633:Applications
606:
597:
576:
557:
553:
528:
504:(green) and
481:
472:
452:
417:control flow
405:
372:
368:
360:
356:
350:
337:
297:
275:memory space
270:
264:
232:
218:
213:
205:
194:
170:
142:
72:
71:
55:
27:received an
20:
1868:10261/40441
1339:Travisano M
1337:Lenski RE,
1269:10261/32513
1208:10261/38513
613:mutualistic
210:replication
150:coevolution
100:competition
2043:Categories
673:References
582:infections
516:(left) or
459:logic task
381:CPU cycles
343:insertions
316:CPU cycles
288:"cheaters"
273:allocated
202:stochastic
189:host-phage
120:phenotypes
108:parasitism
62:prediction
41:pollinator
33:Madagascar
2030:206526054
1754:205992838
1701:204995208
1609:CiteSeerX
1519:Physica D
1286:205220918
984:Evolution
759:Q21045429
733:1553-734X
689:license (
686:CC BY 4.0
622:Selection
560:community
544:Infection
463:phenotype
433:mutations
409:registers
365:offspring
347:deletions
298:In 1993,
271:privately
229:Coreworld
158:food webs
128:behaviors
112:mutualism
104:predation
48:proboscis
2022:20705861
1971:22822151
1920:10821283
1885:18531791
1877:21749596
1808:20561821
1775:: 79–90.
1746:19852618
1714:219-226.
1693:10458160
1639:15128560
1631:15107231
1576:Archived
1495:22282803
1438:21709225
1278:20520609
1217:17713534
1156:22388815
1113:27054443
1105:20950142
1012:28568694
969:10381869
926:21273479
875:19119876
837:19414476
755:Wikidata
751:23533370
703:(2013).
506:parasite
386:Avida-ED
352:pointers
243:Core War
139:Overview
116:networks
81:evolving
2079:Ecology
2002:Bibcode
1994:Science
1979:2728109
1951:Bibcode
1943:Science
1928:4319289
1847:Bibcode
1673:Bibcode
1652:Devolab
1527:Bibcode
1486:3306806
1465:Bibcode
1457:Science
1429:3136311
1387:8041701
1355:Bibcode
1312:Bibcode
1248:Bibcode
1225:4338215
1187:Bibcode
1136:Bibcode
1055:1941243
1035:Bibcode
1027:Ecology
1004:2408229
949:Science
934:9114471
906:Bibcode
898:Science
883:9632335
828:2690506
742:3605903
643:natural
539:threads
514:modular
490:genomes
437:mutants
224:History
98:(e.g.,
86:(i.e.,
46:with a
37:nectary
2028:
2020:
1977:
1969:
1926:
1918:
1900:Nature
1883:
1875:
1806:
1752:
1744:
1699:
1691:
1665:Nature
1637:
1629:
1611:
1493:
1483:
1436:
1426:
1385:
1375:
1284:
1276:
1240:Nature
1223:
1215:
1179:Nature
1164:222676
1162:
1154:
1128:Nature
1111:
1103:
1053:
1010:
1002:
967:
932:
924:
881:
873:
835:
825:
757:
749:
739:
731:
607:While
518:nested
425:memory
413:stacks
261:Tierra
255:Tierra
164:, and
110:, and
79:, and
52:pollen
35:whose
29:orchid
25:Darwin
2026:S2CID
1975:S2CID
1924:S2CID
1881:S2CID
1750:S2CID
1697:S2CID
1635:S2CID
1378:44287
1282:S2CID
1221:S2CID
1160:S2CID
1109:S2CID
1051:JSTOR
1000:JSTOR
930:S2CID
879:S2CID
535:hosts
308:Avida
294:Avida
31:from
23:When
2018:PMID
1967:PMID
1916:PMID
1873:PMID
1804:PMID
1742:PMID
1689:PMID
1627:PMID
1491:PMID
1434:PMID
1383:PMID
1274:PMID
1213:PMID
1152:PMID
1101:PMID
1008:PMID
965:PMID
922:PMID
871:PMID
833:PMID
747:PMID
729:ISSN
697:):
691:2013
611:and
502:host
454:NAND
411:and
345:and
284:code
175:via
44:moth
2010:doi
1998:329
1959:doi
1947:337
1908:doi
1904:405
1863:hdl
1855:doi
1796:doi
1734:doi
1730:174
1681:doi
1669:400
1619:doi
1535:doi
1481:PMC
1473:doi
1461:335
1424:PMC
1414:doi
1410:108
1373:PMC
1363:doi
1320:doi
1264:hdl
1256:doi
1244:465
1203:hdl
1195:doi
1183:448
1144:doi
1132:483
1093:doi
1089:176
1043:doi
992:doi
957:doi
953:284
914:doi
902:331
863:doi
859:173
823:PMC
815:doi
811:364
784:doi
737:PMC
719:doi
693:) (
645:or
529:In
310:at
94:as
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2024:.
2016:.
2008:.
1996:.
1973:.
1965:.
1957:.
1945:.
1922:.
1914:.
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1871:.
1861:.
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1625:.
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
