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separated by fitness valleys in such multidimensional landscapes, or whether they are connected by vastly long neutral ridges. Additionally, the fitness landscape is not static in time but dependent on the changing environment and evolution of other genes. It is hence more of a seascape, further affecting how separated adaptive peaks can actually be. Additionally, it is relevant to take into account that a landscape is in general not an absolute but a relative function. Finally, since it is common to use function as a proxy for fitness when discussing enzymes, any promiscuous activities exist as overlapping landscapes that together will determine the ultimate fitness of the organism, implying a gap between different coexisting relative landscapes.
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formed by the expected fitness at each point. If fitness changes with time (dynamic optimisation) or with other species in the environment (co-evolution), it can still be useful to reason about the trajectories of the instantaneous fitness landscape. However, in some cases (for example, preference-based interactive evolutionary computation) the relevance is more limited, because there is no guarantee that human preferences are consistent with a single fitness assignment.
47:
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landscapes can then be used relative to each other to visually represent the relevant features. Additionally, fitness landscapes of small subsets of evolutionary pathways may be experimentally constructed and visualized, potentially revealing features such as fitness peaks and valleys. Fitness landscapes of evolutionary pathways indicate the probable evolutionary steps and endpoints among sets of individual mutations.
602:
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589:
678:). This fitness is the "height" of the landscape. Genotypes which are similar are said to be "close" to each other, while those that are very different are "far" from each other. The set of all possible genotypes, their degree of similarity, and their related fitness values is then called a fitness landscape. The idea of a fitness landscape is a metaphor to help explain
884:
Even in cases where a fitness function is hard to define, the concept of a fitness landscape can be useful. For example, if fitness evaluation is by stochastic sampling, then sampling is from a (usually unknown) distribution at each point; nevertheless is can be useful to reason about the landscape
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Fitness landscapes are often conceived of as ranges of mountains. There exist local peaks (points from which all paths are downhill, i.e. to lower fitness) and valleys (regions from which many paths lead uphill). A fitness landscape with many local peaks surrounded by deep valleys is called rugged.
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Evolutionary optimization techniques are particularly useful in situations in which it is easy to determine the quality of a single solution, but hard to go through all possible solutions one by one (it is easy to determine the driving time for a particular route of the delivery truck, but it is
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With these limitations in mind, fitness landscapes can still be an instructive way of thinking about evolution. It is fundamentally possible to measure (even if not to visualise) some of the parameters of landscape ruggedness and of peak number, height, separation, and clustering. Simplified 3D
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Several important caveats exist. Since the human mind struggles to think in greater than three dimensions, 3D topologies can mislead when discussing highly multi-dimensional fitness landscapes. In particular it is not clear whether peaks in natural biological fitness landscapes are ever truly
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Note that a local optimum cannot always be found even in evolutionary time: if the local optimum can be found in a reasonable amount of time then the fitness landscape is called "easy" and if the time required is exponential then the fitness landscape is called "hard". Hard landscapes are
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characterized by the maze-like property by which an allele that was once beneficial becomes deleterious, forcing evolution to backtrack. However, the presence of the maze-like property in biophysically inspired fitness landscapes may not be sufficient to generate a hard landscape.
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In the third kind of fitness landscape, each dimension represents a different phenotypic trait. Under the assumptions of quantitative genetics, these phenotypic dimensions can be mapped onto genotypes. See the visualizations below for examples of phenotype to fitness landscapes.
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877:. The best, or at least a very good, solution is then found in the following way: initially, a population of random solutions is created. Then, the solutions are mutated and selected for those with higher fitness, until a satisfying solution has been found.
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Sketch of a fitness landscape. The arrows indicate the preferred flow of a population on the landscape, and the points A and C are local optima. The red ball indicates a population that has moved from a very low fitness value to the top of a
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problems) by imitating the dynamics of biological evolution. For example, a delivery truck with a number of destination addresses can take a large variety of different routes, but only very few will result in a short driving time.
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Wright's mathematical work described fitness as a function of allele frequencies. Here, each dimension describes an allele frequency at a different gene, and goes between 0 and 1.
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falls into this category of fitness landscape. Newer network analysis techniques such as selection-weighted attraction graphing (SWAG) also use a dimensionless genotype space.
633:
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729:. There are three distinct ways of characterizing the other dimensions, though in each case distance represents and is a metaphor for degree of dissimilarity.
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prefer the notion that fitness is being maximized. Therefore, taking the inverse of a potential function turns it into a fitness function, and vice versa.
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typically climbs uphill in the fitness landscape, by a series of small genetic changes, until – in the infinite time limit – a local optimum is reached.
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Poelwijk, Frank J; Kiviet, Daniel J; Weinreich, Daniel M; Tans, Sander J (2007). "Empirical fitness landscapes reveal accessible evolutionary paths".
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Counterbalance: Evolution as movement through a fitness landscape—an interesting (if flawed) discussion of evolution and fitness landscapes
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to the problem of interest (i.e., every possible route in the case of the delivery truck) how 'good' it is. This is done by introducing a
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Mustonen, Ville; Lässig, Michael (2009). "From fitness landscapes to seascapes: Non-equilibrium dynamics of selection and adaptation".
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can be a more complicated object, for example a list of destination addresses in the case of the delivery truck), which is called the
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The idea of studying evolution by visualizing the distribution of fitness values as a kind of landscape was first introduced by
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1080:"Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and Mazes"
756:. The arrows represent various mutational paths that the population could follow while evolving on the fitness landscape.
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769:. No continuous genotype "dimension" is defined. Instead, a network of genotypes are connected via mutational paths.
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Woodcock, Glenn; Higgs, Paul G (1996). "Population
Evolution on a Multiplicative Single-Peak Fitness Landscape".
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Apart from the field of evolutionary biology, the concept of a fitness landscape has also gained importance in
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almost impossible to check all possible routes once the number of destinations grows to more than a handful).
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2015:
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Beerenwinkel, Niko; Pachter, Lior; Sturmfels, Bernd (2007). "Epistasis and Shapes of
Fitness Landscapes".
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have the same replication rate, on the other hand, a fitness landscape is said to be flat. An evolving
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In order to use many common forms of evolutionary optimization, one has to define for every possible
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Diaz Ochoa, Juan G (2017). "Elastic Multi-scale
Mechanisms: Computation and Biological Evolution".
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Bukkuri A, Pienta KJ, Hockett I, Austin RH, Hammarlund EU, Amend SR, Brown JS (February 2023).
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Superimposing evolutionary trajectories onto fitness landscapes in virtual reality
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At Home in the
Universe: The Search for Laws of Self-Organization and Complexity
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Model used to visualise relationship between genotypes and reproductive success
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967:"The roles of mutation, inbreeding, crossbreeding, and selection in evolution"
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816:. In evolutionary optimization, one tries to solve real-world problems (e.g.,
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Pleiotropy Blog—an interesting discussion of Sergey
Gavrilets's contributions
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2006:
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The
Geometry of Evolution: Adaptive Landscapes and Theoretical Morphospaces
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In all fitness landscapes, height represents and is a visual metaphor for
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traditionally think in terms of minimizing the potential function, while
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Kaplan, Jonathan (2008). "The end of the adaptive landscape metaphor?".
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686:, including exploits and glitches in animals like their reactions to
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Recent
Advances in the Theory and Application of Fitness Landscapes
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Video: Using fitness landscapes to visualize evolution in action
1031:"Computational Complexity as an Ultimate Constraint on Evolution"
2244:
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Evolution 101—Shifting
Balance Theory (Figure at bottom of page)
2401:
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has a well-defined replication rate (often referred to as
1835:"Modeling cancer's ecological and evolutionary dynamics"
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is a good solution. In the case of the delivery truck,
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could be the number of deliveries per hour on route
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1134:"Environmental changes bridge evolutionary valleys"
1517:Richter, Hendrik; Engelbrecht, Andries P. (2014).
892:also corresponds to the concept of a potential or
704:problems, fitness landscapes are evaluations of a
662:) are used to visualize the relationship between
888:The concept of a scalar valued fitness function
1127:
1125:
1123:
2413:
1892:
1466:BEACON Blog—Evolution 101: Fitness Landscapes
627:
8:
1628:Fitness landscapes and the origin of species
1078:Bertram, Jason; Masel, Joanna (April 2020).
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1455:Examples of visualized fitness landscapes
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991:
989:
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708:for all candidate solutions (see below).
1506:"No peak in sight for evolving bacteria"
1188:
960:
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765:Wright visualized a genotype space as a
2730:
954:
900:. The two concepts only differ in that
850:is a simple number, such as 0.3, while
37:
2713:Index of evolutionary biology articles
2389:Index of evolutionary biology articles
998:Sewall Wright and Evolutionary Biology
783:Allele frequency to fitness landscapes
752:Visualization of two dimensions of an
1690:An Introduction to Genetic Algorithms
7:
1132:Steinberg, B; Ostermeier, M (2016).
2199:Evolutionary developmental biology
1763:10.1093/oso/9780195079517.001.0001
1717:Foundations of Genetic Programming
25:
2733:
2156:Evolution of sexual reproduction
1711:Langdon, W.B.; Poli, R. (2002).
1219:10.1111/j.1558-5646.2011.01236.x
1203:"Visualizing Fitness Landscapes"
929:
924:
601:
600:
587:
45:
2756:Modern synthesis (20th century)
1000:. University of Chicago Press.
791:Phenotype to fitness landscapes
594:Evolutionary biology portal
2513:Constructive neutral evolution
1927:Genotype–phenotype distinction
1631:. Princeton University Press.
1403:Journal of Molecular Evolution
1360:Journal of Theoretical Biology
1256:. Cambridge University Press.
761:Genotype to fitness landscapes
553:Creation–evolution controversy
307:History of evolutionary theory
1:
2184:Regulation of gene expression
2463:Fisher's fundamental theorem
2354:Endless Forms Most Beautiful
2134:Evolution of genetic systems
1942:Gene–environment correlation
1937:Gene–environment interaction
1719:. Springer. pp. 17–26.
1512:. Michigan State University.
1201:McCandlish, David M (2011).
996:Provine, William B. (1986).
800:In evolutionary optimization
538:Evolution as fact and theory
2488:Coefficient of relationship
2333:Christiane NĂĽsslein-Volhard
1757:. Oxford University Press.
1725:10.1007/978-3-662-04726-2_2
1661:. Oxford University Press.
1476:Pup Fish Evolution—UC Davis
1096:10.1534/genetics.120.302815
1047:10.1534/genetics.119.302000
1029:Kaznatcheev, Artem (2019).
670:. It is assumed that every
2782:
2209:Hedgehog signaling pathway
2086:Developmental architecture
1851:10.1007/s12032-023-01968-0
1625:Gavrilets, Sergey (2004).
1250:McGhee, George R. (2006).
846:(scalar valued means that
573:Nature-nurture controversy
2708:
2483:Coefficient of inbreeding
2386:
2036:Transgressive segregation
1599:Climbing Mount Improbable
1529:10.1007/978-3-642-41888-4
1423:10.1007/s00239-017-9823-7
1337:10.1016/j.tig.2009.01.002
1302:10.1007/s10539-008-9116-z
806:evolutionary optimization
702:evolutionary optimization
460:Evolutionary neuroscience
435:Evolutionary epistemology
415:Evolutionary anthropology
395:Applications of evolution
2661:Evolutionary game theory
2443:Hardy–Weinberg principle
1290:Biology & Philosophy
450:Evolutionary linguistics
445:Evolutionary game theory
420:Evolutionary computation
2473:Shifting balance theory
2214:Notch signaling pathway
2189:Gene regulatory network
2072:Dual inheritance theory
1713:"2. Fitness Landscapes"
965:Wright, Sewall (1932).
912:Caveats and limitations
660:evolutionary landscapes
563:Objections to evolution
470:Evolutionary psychology
465:Evolutionary physiology
410:Evolutionary aesthetics
389:Fields and applications
371:History of paleontology
2458:Linkage disequilibrium
2262:cis-regulatory element
2170:Control of development
2050:Non-genetic influences
2016:evolutionary landscape
1380:10.1006/jtbi.1996.0049
1158:10.1126/sciadv.1500921
757:
722:
495:Speciation experiments
475:Experimental evolution
430:Evolutionary economics
252:Recent human evolution
110:Processes and outcomes
2700:Quantitative genetics
2609:Balding–Nichols model
2594:Population bottleneck
2589:Small population size
2493:Selection coefficient
2373:Nature versus nurture
2277:Cell surface receptor
2194:Evo-devo gene toolkit
2093:Developmental biology
2031:Polygenic inheritance
1957:Quantitative genetics
751:
719:
455:Evolutionary medicine
400:Biosocial criminology
366:History of speciation
279:Evolutionary taxonomy
242:Timeline of evolution
2740:Evolutionary biology
2571:Background selection
2558:on genomic variation
2556:Effects of selection
2508:Population structure
2282:Transcription factor
1997:Genetic assimilation
1984:Genetic architecture
1755:The Origins of Order
1504:Cameron, L. (2023).
814:evolution strategies
754:NK fitness landscape
668:reproductive success
648:evolutionary biology
425:Evolutionary ecology
39:Evolutionary biology
2761:Population genetics
2690:Population genomics
2566:Genetic hitchhiking
2453:Identity by descent
2429:Population genetics
2378:Morphogenetic field
2295:Influential figures
1810:10.1038/nature05451
1802:2007Natur.445..383P
1578:2006q.bio.....3034B
1415:2018JMolE..86...47D
1372:1996JThBi.179...61W
1150:2016SciA....2E0921S
688:supernormal stimuli
656:adaptive landscapes
527:Social implications
515:Universal Darwinism
505:Island biogeography
440:Evolutionary ethics
405:Ecological genetics
351:Molecular evolution
289:Transitional fossil
117:Population genetics
33:Part of a series on
2676:Landscape genetics
2067:Genomic imprinting
1325:Trends in Genetics
943:Viral quasispecies
810:genetic algorithms
758:
723:
652:fitness landscapes
558:Theistic evolution
490:Selective breeding
202:Parallel evolution
167:Adaptive radiation
18:Fitness landscapes
2721:
2720:
2671:Genetic genealogy
2666:Fitness landscape
2395:
2394:
2328:Eric F. Wieschaus
2290:
2289:
2108:Pattern formation
2012:Fitness landscape
1772:978-0-19-507951-7
1703:978-0-262-63185-3
1685:Mitchell, Melanie
1668:978-0-19-511130-9
1638:978-0-691-11983-0
1556:Statistica Sinica
1538:978-3-642-41888-4
1263:978-1-139-45995-2
684:natural selection
644:
643:
335:Origin of Species
137:Natural selection
16:(Redirected from
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2738:
2737:
2729:
2630:J. B. S. Haldane
2422:
2415:
2408:
2399:
2338:William McGinnis
2307:Richard Lewontin
2302:C. H. Waddington
2174:
2151:Neutral networks
1901:
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1872:
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1829:
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1751:Kauffman, Stuart
1746:
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1655:Kauffman, Stuart
1650:
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1594:Dawkins, Richard
1589:
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1569:q-bio.PE/0603034
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1138:Science Advances
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856:fitness function
808:methods such as
706:fitness function
682:in evolution by
636:
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609:
604:
603:
596:
592:
591:
568:Level of support
361:Current research
346:Modern synthesis
341:Before synthesis
294:Extinction event
52:Darwin's finches
49:
30:
21:
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2448:Genetic linkage
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2348:Sean B. Carroll
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2062:Maternal effect
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1978:
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1796:(7126): 383–6.
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1492:Further reading
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1144:(1): e1500921.
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894:energy function
802:
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773:Stuart Kauffman
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247:Human evolution
237:History of life
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220:Natural history
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2695:Phylogeography
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2685:Microevolution
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2644:Related topics
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2599:Founder effect
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2510:
2505:
2500:
2495:
2490:
2485:
2480:
2478:Price equation
2475:
2470:
2468:Neutral theory
2465:
2460:
2455:
2450:
2445:
2439:
2437:
2433:
2432:
2427:
2425:
2424:
2417:
2410:
2402:
2393:
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2360:
2359:
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2357:
2345:
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2330:
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2324:
2323:
2312:François Jacob
2309:
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2298:
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2279:
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2201:
2196:
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2180:
2178:
2171:
2167:
2166:
2164:
2163:
2158:
2153:
2148:
2143:
2137:
2135:
2131:
2130:
2128:
2127:
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2117:
2112:
2111:
2110:
2105:
2095:
2089:
2087:
2083:
2082:
2080:
2079:
2074:
2069:
2064:
2059:
2053:
2051:
2047:
2046:
2044:
2043:
2041:Sequence space
2038:
2033:
2028:
2023:
2018:
2009:
2004:
1999:
1994:
1988:
1986:
1980:
1979:
1977:
1976:
1971:
1970:
1969:
1959:
1954:
1949:
1944:
1939:
1934:
1929:
1923:
1921:
1917:
1916:
1906:
1904:
1903:
1896:
1889:
1881:
1874:
1873:
1830:
1785:
1771:
1747:
1733:
1708:
1702:
1681:
1667:
1651:
1637:
1622:
1608:
1590:
1562:(4): 1317–42.
1551:
1537:
1514:
1501:
1495:
1494:
1493:
1489:
1488:
1483:
1478:
1473:
1468:
1463:
1457:
1456:
1451:
1450:External links
1448:
1445:
1444:
1393:
1350:
1315:
1280:
1277:Gavrilets 2004
1269:
1262:
1242:
1213:(6): 1544–58.
1193:
1181:
1119:
1090:(4): 1047–57.
1070:
1041:(1): 245–265.
1021:
1006:
983:
953:
952:
950:
947:
946:
945:
938:
935:
913:
910:
801:
798:
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759:
713:
710:
642:
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631:
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616:
613:
612:
611:
610:
597:
580:
579:
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575:
570:
565:
560:
555:
550:
548:Social effects
545:
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529:
526:
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522:
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518:
517:
512:
507:
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497:
492:
487:
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276:
274:Classification
271:
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256:
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244:
239:
234:
232:Common descent
229:
227:Origin of life
223:
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35:
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14:
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2:
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2659:
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2649:
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2646:
2642:
2636:
2635:Sewall Wright
2633:
2631:
2628:
2626:
2623:
2622:
2620:
2616:
2610:
2607:
2605:
2602:
2600:
2597:
2595:
2592:
2590:
2587:
2586:
2584:
2582:
2581:Genetic drift
2578:
2572:
2569:
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2564:
2563:
2561:
2553:
2547:
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2418:
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2411:
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2404:
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2400:
2390:
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2379:
2376:
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2368:
2364:
2356:
2355:
2351:
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2341:
2339:
2336:
2334:
2331:
2329:
2326:
2322:
2319:
2318:
2317:
2316:Jacques Monod
2313:
2310:
2308:
2305:
2303:
2300:
2299:
2297:
2293:
2283:
2280:
2278:
2275:
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2268:
2265:
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2258:
2255:
2253:
2250:
2246:
2243:
2242:
2241:
2238:
2236:
2233:
2231:
2230:Homeotic gene
2228:
2227:
2225:
2221:
2215:
2212:
2210:
2207:
2205:
2202:
2200:
2197:
2195:
2192:
2190:
2187:
2185:
2182:
2181:
2179:
2175:
2172:
2168:
2162:
2159:
2157:
2154:
2152:
2149:
2147:
2144:
2142:
2139:
2138:
2136:
2132:
2126:
2123:
2121:
2118:
2116:
2113:
2109:
2106:
2104:
2101:
2100:
2099:
2098:Morphogenesis
2096:
2094:
2091:
2090:
2088:
2084:
2078:
2075:
2073:
2070:
2068:
2065:
2063:
2060:
2058:
2055:
2054:
2052:
2048:
2042:
2039:
2037:
2034:
2032:
2029:
2027:
2024:
2022:
2019:
2017:
2013:
2010:
2008:
2005:
2003:
2000:
1998:
1995:
1993:
1990:
1989:
1987:
1985:
1981:
1975:
1972:
1968:
1965:
1964:
1963:
1960:
1958:
1955:
1953:
1950:
1948:
1945:
1943:
1940:
1938:
1935:
1933:
1932:Reaction norm
1930:
1928:
1925:
1924:
1922:
1918:
1914:
1910:
1902:
1897:
1895:
1890:
1888:
1883:
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1879:
1870:
1866:
1861:
1856:
1852:
1848:
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1836:
1831:
1827:
1823:
1819:
1815:
1811:
1807:
1803:
1799:
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1791:
1786:
1782:
1778:
1774:
1768:
1764:
1760:
1756:
1752:
1748:
1744:
1740:
1736:
1734:3-540-42451-2
1730:
1726:
1722:
1718:
1714:
1709:
1705:
1699:
1696:. MIT Press.
1692:
1691:
1686:
1682:
1678:
1674:
1670:
1664:
1660:
1656:
1652:
1648:
1644:
1640:
1634:
1630:
1629:
1623:
1619:
1615:
1611:
1609:0-393-03930-7
1605:
1601:
1600:
1595:
1591:
1587:
1583:
1579:
1575:
1570:
1565:
1561:
1557:
1552:
1548:
1544:
1540:
1534:
1530:
1526:
1522:
1521:
1515:
1511:
1507:
1502:
1500:
1497:
1496:
1491:
1490:
1487:
1484:
1482:
1479:
1477:
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1472:
1469:
1467:
1464:
1462:
1459:
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1453:
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1432:
1428:
1424:
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1354:
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1342:
1338:
1334:
1330:
1326:
1319:
1316:
1311:
1307:
1303:
1299:
1296:(5): 625–38.
1295:
1291:
1284:
1281:
1278:
1273:
1270:
1265:
1259:
1255:
1254:
1246:
1243:
1238:
1234:
1229:
1224:
1220:
1216:
1212:
1208:
1204:
1197:
1194:
1190:
1189:Kauffman 1993
1185:
1182:
1177:
1173:
1168:
1163:
1159:
1155:
1151:
1147:
1143:
1139:
1135:
1128:
1126:
1124:
1120:
1115:
1111:
1106:
1101:
1097:
1093:
1089:
1085:
1081:
1074:
1071:
1066:
1062:
1057:
1052:
1048:
1044:
1040:
1036:
1032:
1025:
1022:
1017:
1013:
1009:
1007:0-226-68473-3
1003:
999:
992:
990:
988:
984:
979:
975:
968:
961:
959:
955:
948:
944:
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940:
936:
934:
932:
927:
922:
918:
911:
909:
907:
903:
899:
895:
891:
886:
882:
878:
876:
872:
868:
865:implies that
864:
859:
857:
853:
849:
845:
842:
838:
834:
831:
826:
823:
819:
815:
811:
807:
799:
797:
790:
788:
782:
780:
778:
774:
770:
768:
760:
755:
750:
746:
742:
740:
736:
730:
728:
718:
711:
709:
707:
703:
698:
696:
695:Sewall Wright
691:
689:
685:
681:
677:
673:
669:
665:
661:
657:
653:
649:
637:
632:
630:
625:
623:
618:
617:
615:
614:
608:
598:
595:
590:
584:
583:
582:
581:
574:
571:
569:
566:
564:
561:
559:
556:
554:
551:
549:
546:
544:
541:
539:
536:
534:
531:
530:
524:
523:
516:
513:
511:
508:
506:
503:
501:
498:
496:
493:
491:
488:
486:
483:
481:
480:Phylogenetics
478:
476:
473:
471:
468:
466:
463:
461:
458:
456:
453:
451:
448:
446:
443:
441:
438:
436:
433:
431:
428:
426:
423:
421:
418:
416:
413:
411:
408:
406:
403:
401:
398:
396:
393:
392:
386:
385:
376:
372:
369:
367:
364:
362:
359:
357:
354:
352:
349:
347:
344:
342:
339:
337:
336:
332:
330:
327:
325:
324:Before Darwin
322:
320:
317:
315:
312:
311:
304:
303:
295:
292:
290:
287:
285:
282:
280:
277:
275:
272:
270:
267:
265:
262:
260:
257:
253:
250:
249:
248:
245:
243:
240:
238:
235:
233:
230:
228:
225:
224:
217:
216:
208:
205:
203:
200:
198:
195:
193:
190:
188:
185:
183:
180:
178:
175:
173:
170:
168:
165:
163:
160:
158:
155:
153:
152:Genetic drift
150:
148:
145:
143:
140:
138:
135:
133:
130:
128:
125:
123:
120:
118:
115:
114:
107:
106:
100:
97:
95:
92:
90:
87:
86:
83:
80:
78:
75:
73:
70:
68:
65:
64:
62:
61:
57:
53:
48:
44:
43:
40:
36:
32:
31:
19:
2665:
2651:Biogeography
2625:R. A. Fisher
2503:Heritability
2436:Key concepts
2352:
2245:eyeless gene
2141:Evolvability
2115:Segmentation
2011:
1992:Canalisation
1962:Heterochrony
1952:Heritability
1920:Key concepts
1842:
1838:
1793:
1789:
1754:
1716:
1689:
1658:
1627:
1597:
1559:
1555:
1523:. Springer.
1519:
1509:
1409:(1): 47–57.
1406:
1402:
1396:
1366:(1): 61–73.
1363:
1359:
1353:
1331:(3): 111–9.
1328:
1324:
1318:
1293:
1289:
1283:
1272:
1252:
1245:
1210:
1206:
1196:
1191:, p. 43
1184:
1141:
1137:
1087:
1083:
1073:
1038:
1034:
1024:
997:
980:(8): 355–66.
977:
973:
923:
919:
915:
889:
887:
883:
879:
874:
870:
866:
862:
860:
851:
847:
843:
832:
827:
803:
794:
786:
771:
764:
743:
731:
724:
699:
692:
680:flawed forms
655:
651:
645:
500:Sociobiology
485:Paleontology
333:
269:Biogeography
264:Biodiversity
182:Coextinction
172:Co-operation
147:Polymorphism
72:Introduction
2604:Coalescence
2343:Mike Levine
2252:Distal-less
2077:Polyphenism
2057:Epigenetics
1909:development
818:engineering
510:Systematics
319:Renaissance
197:Convergence
187:Contingency
177:Coevolution
2750:Categories
2546:Ecological
2536:Artificial
2321:Lac operon
2146:Robustness
2125:Modularity
2120:Metamerism
2026:Plasticity
2021:Pleiotropy
1974:Heterotopy
1845:(4): 109.
1781:1410954535
1647:1044748197
1602:. Norton.
1547:7324758532
1016:1036863434
949:References
906:biologists
902:physicists
739:population
712:In biology
658:(types of
284:Cladistics
207:Extinction
192:Divergence
162:Speciation
142:Adaptation
56:John Gould
2766:Selection
2656:Evolution
2523:Selection
2272:Morphogen
2257:Engrailed
2240:Pax genes
2161:Tinkering
2007:Epistasis
2002:Dominance
1913:phenotype
1839:Med Oncol
1743:828735699
1677:923535473
1618:757336308
1310:170649453
1207:Evolution
822:logistics
767:hypercube
735:genotypes
697:in 1932.
664:genotypes
543:Dysgenics
259:Phylogeny
157:Gene flow
127:Diversity
122:Variation
2680:genomics
2618:Founders
2235:Hox gene
2223:Elements
2204:Homeobox
1869:36853375
1818:17251971
1753:(1993).
1687:(1996).
1657:(1995).
1596:(1996).
1510:MSUToday
1439:22624633
1431:29248946
1345:19232770
1237:21644947
1176:26844293
1114:32107278
1084:Genetics
1065:30833289
1035:Genetics
937:See also
841:function
839:-valued
830:solution
777:NK model
672:genotype
607:Category
533:Eugenics
375:timeline
356:Evo-devo
314:Overview
132:Mutation
94:Evidence
89:Glossary
2531:Natural
2498:Fitness
2366:Debates
2177:Systems
2103:Eyespot
1967:Neoteny
1860:9974726
1826:4415468
1798:Bibcode
1586:2398598
1574:Bibcode
1411:Bibcode
1388:8733432
1368:Bibcode
1228:3668694
1167:4737206
1146:Bibcode
1105:7153934
1056:6499524
898:physics
861:A high
733:If all
727:fitness
676:fitness
99:History
82:Outline
2726:Portal
2541:Sexual
2267:Ligand
1947:Operon
1867:
1857:
1824:
1816:
1790:Nature
1779:
1769:
1741:
1731:
1700:
1675:
1665:
1645:
1635:
1616:
1606:
1584:
1545:
1535:
1437:
1429:
1386:
1343:
1308:
1260:
1235:
1225:
1174:
1164:
1112:
1102:
1063:
1053:
1014:
1004:
837:scalar
605:
329:Darwin
1822:S2CID
1694:(PDF)
1564:arXiv
1435:S2CID
1306:S2CID
970:(PDF)
721:peak.
67:Index
2678:and
1907:The
1865:PMID
1814:PMID
1777:OCLC
1767:ISBN
1739:OCLC
1729:ISBN
1698:ISBN
1673:OCLC
1663:ISBN
1643:OCLC
1633:ISBN
1614:OCLC
1604:ISBN
1543:OCLC
1533:ISBN
1427:PMID
1384:PMID
1341:PMID
1258:ISBN
1233:PMID
1172:PMID
1110:PMID
1061:PMID
1012:OCLC
1002:ISBN
890:f(s)
871:f(s)
863:f(s)
848:f(s)
844:f(s)
666:and
77:Main
1911:of
1855:PMC
1847:doi
1806:doi
1794:445
1759:doi
1721:doi
1525:doi
1419:doi
1376:doi
1364:179
1333:doi
1298:doi
1223:PMC
1215:doi
1162:PMC
1154:doi
1100:PMC
1092:doi
1088:214
1051:PMC
1043:doi
1039:212
896:in
820:or
812:or
775:'s
700:In
654:or
646:In
54:by
2752::
2314:+
1863:.
1853:.
1843:40
1841:.
1837:.
1820:.
1812:.
1804:.
1792:.
1775:.
1765:.
1737:.
1727:.
1715:.
1671:.
1641:.
1612:.
1582:MR
1580:.
1572:.
1560:17
1558:.
1541:.
1531:.
1508:.
1433:.
1425:.
1417:.
1407:86
1405:.
1382:.
1374:.
1362:.
1339:.
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