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Journal articles on the topic 'Evolutionary models'

To see the other types of publications on this topic, follow the link: Evolutionary models.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Han, Z., and Ph Podsiadlowski. "Binary Evolutionary Models." Proceedings of the International Astronomical Union 4, S252 (April 2008): 349–57. http://dx.doi.org/10.1017/s1743921308023193.

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AbstractIn this talk, we present the general principles of binary evolution and give two examples. The first example is the formation of subdwarf B stars (sdBs) and their application to the long-standing problem of ultraviolet excess (also known as UV-upturn) in elliptical galaxies. The second is for the progenitors of type Ia supernovae (SNe Ia). We discuss the main binary interactions, i.e., stable Roche lobe overflow (RLOF) and common envelope (CE) evolution, and show evolutionary channels leading to the formation of various binary-related objects. In the first example, we show that the binary model of sdB stars of Han et al. (2002, 2003) can reproduce field sdB stars and their counterparts, extreme horizontal branch (EHB) stars, in globular clusters. By applying the binary model to the study of evolutionary population synthesis, we have obtained an “a priori” model for the UV-upturn of elliptical galaxies and showed that the UV-upturn is most likely resulted from binary interactions. This has major implications for understanding the evolution of the UV excess and elliptical galaxies in general. In the second example, we introduce the single degenerate channel and the double degenerate channel for the progenitors of SNe Ia. We give the birth rates and delay time distributions for each channel and the distributions of companion stars at the moment of SN explosion for the single degenerate channel, which would help to search for the remnant companion stars observationally.

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2

Sullivan, Roger J., and Henry F. Lyle, III. "Economic models are not evolutionary models." Behavioral and Brain Sciences 28, no. 6 (December 2005): 836. http://dx.doi.org/10.1017/s0140525x05430149.

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Henrich et al. reject the “selfishness axiom” within a narrowly-defined economic model, and are premature in claiming that they have demonstrated cross-cultural variability in “selfishness” as defined in broader evolutionary theory. We also question whether a key experimental condition, anonymity, can be maintained in the small, cohesive, social groupings employed in the study.

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3

Moraes, Marcelo Botelho da Costa, and Marcelo Seido Nagano. "Cash Management Policies By Evolutionary Models: A Comparison Using The MILLER-ORR Model." Journal of Information Systems and Technology Management 10, no. 3 (December 30, 2013): 561–76. http://dx.doi.org/10.4301/s1807-17752013000300006.

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4

APALOO, JOSEPH. "FREQUENCY INDEPENDENT EVOLUTIONARY MODELS." Journal of Biological Systems 07, no. 01 (March 1999): 1–9. http://dx.doi.org/10.1142/s0218339099000024.

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Using general classes of evolutionary models in which fitnesses are frequency independent and density dependent, we show that a phenotype is an ESS if and only if it is a NIS. This result is seen to hold in single species and multi species evolutionary models with and without structure. Frequency independent and density dependent evolutionary models are thus more likely to attain an ESS.

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5

van Rensbergen, Walter, Nicki Mennekens, Jean-Pierre de Greve, Kim Jansen, and Bert de Loore. "Evolutionary models of binaries." Proceedings of the International Astronomical Union 6, S272 (July 2010): 486–91. http://dx.doi.org/10.1017/s1743921311011136.

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AbstractWe have put on CDS a catalog containing 561 evolutionary models of binaries: J/A+A/487/1129 (Van Rensbergen+, 2008). The catalog covers a grid of binaries with a B-type primary at birth, different values for the initial mass ratio and a wide range of initial orbital periods. The evolution was calculated with the Brussels code in which we introduced the spinning up and the creation of a hot spot on the gainer or its accretion disk, caused by impacting mass coming from the donor. When the kinetic energy of fast rotation added to the radiative energy of the hot spot exceeds the binding energy, a fraction of the transferred matter leaves the system: the evolution is liberal during a short lasting era of rapid mass transfer. The spin-up of the gainer was modulated using both strong and weak tides. The catalog shows the results for both types. For comparison, we included the evolutionary tracks calculated with the conservative assumption. Binaries with an initial primary below 6 M⊙ show hardly any mass loss from the system and thus evolve conservatively. Above this limit differences between liberal and conservative evolution grow with increasing initial mass of the primary star.

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6

Ponzi, A., and Y. Aizawa. "Evolutionary financial market models." Physica A: Statistical Mechanics and its Applications 287, no. 3-4 (December 2000): 507–23. http://dx.doi.org/10.1016/s0378-4371(00)00389-7.

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7

Burley, Peter. "Evolutionary von Neumann models." Journal of Evolutionary Economics 2, no. 4 (December 1992): 269–80. http://dx.doi.org/10.1007/bf01200126.

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8

Garfield, Zachary H., Robert L. Hubbard, and Edward H. Hagen. "Evolutionary Models of Leadership." Human Nature 30, no. 1 (February 19, 2019): 23–58. http://dx.doi.org/10.1007/s12110-019-09338-4.

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9

Nelson, Gareth. "CLADISTICS AND EVOLUTIONARY MODELS." Cladistics 5, no. 3 (September 1989): 275–89. http://dx.doi.org/10.1111/j.1096-0031.1989.tb00490.x.

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10

Stephenson, Daniel. "Coordination and evolutionary dynamics: When are evolutionary models reliable?" Games and Economic Behavior 113 (January 2019): 381–95. http://dx.doi.org/10.1016/j.geb.2018.10.002.

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11

Trubitsyn, V. P. "Evolutionary models of floating continents." Russian Journal of Earth Sciences 6, no. 5 (October 27, 2004): 311–22. http://dx.doi.org/10.2205/2004es000147.

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12

Scott, Matthew. "Metabolic models predict evolutionary dynamics." Nature Ecology & Evolution 5, no. 5 (March 4, 2021): 560–61. http://dx.doi.org/10.1038/s41559-021-01405-3.

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13

Fernandes, Catarina S., Valérie Van Grootel, Sébastian J. A. J. Salmon, Bernhard Aringer, Adam J. Burgasser, Richard Scuflaire, Pierre Brassard, and Gilles Fontaine. "Evolutionary Models for Ultracool Dwarfs." Astrophysical Journal 879, no. 2 (July 10, 2019): 94. http://dx.doi.org/10.3847/1538-4357/ab2333.

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14

Uhl, E. W., E. Whitley, E. Galbreath, M. McArthur, and M. J. Oglesbee. "Evolutionary Aspects of Animal Models." Veterinary Pathology 49, no. 5 (September 2012): 876–78. http://dx.doi.org/10.1177/0300985812456214.

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15

Pinelis, Iosif. "Evolutionary models of phylogenetic trees." Proceedings of the Royal Society of London. Series B: Biological Sciences 270, no. 1522 (July 7, 2003): 1425–31. http://dx.doi.org/10.1098/rspb.2003.2374.

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16

Kremer, Erhard. "Credibility in evolutionary models revisited." Scandinavian Actuarial Journal 1997, no. 1 (January 1997): 95–96. http://dx.doi.org/10.1080/03461238.1997.10413980.

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17

Xu, Tianbing, Zhongfei Zhang, Philip S. Yu, and Bo Long. "Generative Models for Evolutionary Clustering." ACM Transactions on Knowledge Discovery from Data 6, no. 2 (July 2012): 1–27. http://dx.doi.org/10.1145/2297456.2297459.

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18

Hartmann, Klaas, Dennis Wong, and Tanja Stadler. "Sampling Trees from Evolutionary Models." Systematic Biology 59, no. 4 (May 28, 2010): 465–76. http://dx.doi.org/10.1093/sysbio/syq026.

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19

Arifovic, Jasmina. "EVOLUTIONARY ALGORITHMS IN MACROECONOMIC MODELS." Macroeconomic Dynamics 4, no. 3 (September 2000): 373–414. http://dx.doi.org/10.1017/s1365100500016059.

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This paper provides a survey of the applications of evolutionary algorithms in macroeconomic models. Discussion is organized around the issues related to stability of equilibria, equilibrium selection, transitional dynamics, and the long-run evolutionary dynamics different from rational-expectations equilibrium outcomes. The survey also discusses criteria that can be used to evaluate the performance and usefulness of evolutionary algorithms in the macroeconomic context.

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20

Prasad, Sheo S. "Evolutionary Models of Interstellar Chemistry." Symposium - International Astronomical Union 120 (1987): 259–72. http://dx.doi.org/10.1017/s0074180900154130.

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The goal of evolutionary models of interstellar chemistry is to understand how interstellar clouds came to be the way they are, how they will change with time, and to place them in an evolutionary sequence with other celestial objects such as stars. To this end, we present an improved Mark II version of our earlier model of chemistry in dynamically evolving clouds. The Mark II model suggests that the conventional elemental C/O ratio less than one can explain the observed abundances of CI and the non-detection of O2 in dense clouds. Coupled chemical-dynamical models seem to have the potential to generate many observable discriminators of the evolutionary tracks. This is exciting, because, in general, purely dynamical models do not yield enough verifiable discriminators of the predicted tracks.

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21

Lin, Y. K., and Yan Yong. "Evolutionary Kanai‐Tajimi Earthquake Models." Journal of Engineering Mechanics 113, no. 8 (August 1987): 1119–37. http://dx.doi.org/10.1061/(asce)0733-9399(1987)113:8(1119).

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22

Riska, Bruce. "Regression Models in Evolutionary Allometry." American Naturalist 138, no. 2 (August 1991): 283–99. http://dx.doi.org/10.1086/285218.

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23

Gomulkiewicz, Richard, and Ruth G. Shaw. "Evolutionary rescue beyond the models." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1610 (January 19, 2013): 20120093. http://dx.doi.org/10.1098/rstb.2012.0093.

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Laboratory model systems and mathematical models have shed considerable light on the fundamental properties and processes of evolutionary rescue. But it remains to determine the extent to which these model-based findings can help biologists predict when evolution will fail or succeed in rescuing natural populations that are facing novel conditions that threaten their persistence. In this article, we present a prospectus for transferring our basic understanding of evolutionary rescue to wild and other non-laboratory populations. Current experimental and theoretical results emphasize how the interplay between inheritance processes and absolute fitness in changed environments drive population dynamics and determine prospects of extinction. We discuss the challenge of inferring these elements of the evolutionary rescue process in field and natural settings. Addressing this challenge will contribute to a more comprehensive understanding of population persistence that combines processes of evolutionary rescue with developmental and ecological mechanisms.

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24

Prasad, Sheo S. "Evolutionary Models of Interstellar Chemistry." Symposium - International Astronomical Union 150 (1992): 205–10. http://dx.doi.org/10.1017/s0074180900090021.

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Evolutionary chemical models are ultimately unavoidable for a full understanding of interstellar clouds. They include not only the chemical processes but also the dynamical processes by which the modeled object came to be the way it is. From an evolutionary perspective, dark cores may be ephemeral objects and dynamical equilibrium an exception rather than norm. Evolutionary models have numerous advantages over “classical” fixed condition equilibrium models. They have the potential to provide more elegant explanations for the observed inter-cloud and intra-cloud chemical differences. The problem of the depletion of gas phase molecules by condensation onto the grain may also be less serious in evolutionary models. Hence, these models should be actively developed.

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25

Mühlenbein, Heinz, and Thilo Mahnig. "Evolutionary optimization using graphical models." New Generation Computing 18, no. 2 (June 2000): 157–66. http://dx.doi.org/10.1007/bf03037594.

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26

Roff, Derek A. "Defining fitness in evolutionary models." Journal of Genetics 87, no. 4 (December 2008): 339–48. http://dx.doi.org/10.1007/s12041-008-0056-9.

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27

Alger, Ingela, and Jörgen W. Weibull. "Evolutionary Models of Preference Formation." Annual Review of Economics 11, no. 1 (August 2, 2019): 329–54. http://dx.doi.org/10.1146/annurev-economics-080218-030255.

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The literature on the evolution of preferences of individuals in strategic interactions is vast and diverse. We organize the discussion around the following question: Supposing that material outcomes drive evolutionary success, under what circumstances does evolution promote Homo economicus, defined as material self-interest, and when does it instead lead to other preferences? The literature suggests that Homo economicus is favored by evolution only when individuals’ preferences are their private information and the population is large and well-mixed, so that individuals with rare mutant preferences almost never get to interact with each other. If rare mutants instead interact more often (say, due to local dispersion), then evolution instead favors a certain generalization of Homo economicus including a Kantian concern. If individuals interact under complete information about preferences, then evolution destabilizes Homo economicus in virtually all games.

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28

Bailey, Nathan W. "Evolutionary models of extended phenotypes." Trends in Ecology & Evolution 27, no. 10 (October 2012): 561–69. http://dx.doi.org/10.1016/j.tree.2012.05.011.

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29

Cerviño, M., J. M. Mas-Hesse, and D. Kunth. "Evolutionary synthesis models of starbursts." Astronomy & Astrophysics 392, no. 1 (August 22, 2002): 19–31. http://dx.doi.org/10.1051/0004-6361:20020785.

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30

López, I., M. Gámez, and R. Carreño. "Observability in dynamic evolutionary models." Biosystems 73, no. 2 (February 2004): 99–109. http://dx.doi.org/10.1016/j.biosystems.2003.10.003.

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31

Levine, David K., and Salvatore Modica. "Dynamics in stochastic evolutionary models." Theoretical Economics 11, no. 1 (January 2016): 89–131. http://dx.doi.org/10.3982/te1978.

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32

Egozcue, Juan José, and Eusebi Jarauta-Bragulat. "Differential Models for Evolutionary Compositions." Mathematical Geosciences 46, no. 4 (April 23, 2014): 381–410. http://dx.doi.org/10.1007/s11004-014-9533-2.

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33

Pham, D. T., and M. Castellani. "Evolutionary learning of fuzzy models." Engineering Applications of Artificial Intelligence 19, no. 6 (September 2006): 583–92. http://dx.doi.org/10.1016/j.engappai.2006.01.007.

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34

Skobtsov, Y. A. "DISTRIBUTED EVOLUTIONARY ALGORITHMS - BASIC MODELS." Mathematical Methods in Technologies and Technics, no. 8 (2022): 103–7. http://dx.doi.org/10.52348/2712-8873_mmtt_2022_8_103.

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35

Rees, Mark, and Stephen P. Ellner. "Evolving integral projection models: evolutionary demography meets eco‐evolutionary dynamics." Methods in Ecology and Evolution 7, no. 2 (February 2016): 157–70. http://dx.doi.org/10.1111/2041-210x.12487.

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36

McGlothlin, Joel W., Erol Akçay, Edmund D. Brodie, Allen J. Moore, and Jeremy Van Cleve. "A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution." Journal of Heredity 113, no. 1 (January 1, 2022): 109–19. http://dx.doi.org/10.1093/jhered/esab064.

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Abstract Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory and, in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

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37

Frechette, Layne B., and Robert B. Best. "Evolutionary Models of Fold-Switching Proteins." Biophysical Journal 120, no. 3 (February 2021): 20a. http://dx.doi.org/10.1016/j.bpj.2020.11.383.

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38

Horvath, Dragos, J. Brown, Gilles Marcou, and Alexandre Varnek. "An Evolutionary Optimizer of libsvm Models." Challenges 5, no. 2 (November 24, 2014): 450–72. http://dx.doi.org/10.3390/challe5020450.

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39

Farkas, Andrew. "Evolutionary Models in Foreign Policy Analysis." International Studies Quarterly 40, no. 3 (September 1996): 343. http://dx.doi.org/10.2307/2600715.

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40

Csányi, Vilmos. "Evolutionary models in the social sciences." World Futures 34, no. 1-2 (June 1992): 1–2. http://dx.doi.org/10.1080/02604027.1992.9972289.

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41

Csányi, Vilmos. "Natural sciences and the evolutionary models." World Futures 34, no. 1-2 (June 1992): 15–24. http://dx.doi.org/10.1080/02604027.1992.9972291.

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42

KOSS, MARY P. "Evolutionary Models of Why Men Rape." Trauma, Violence, & Abuse 1, no. 2 (April 2000): 182–90. http://dx.doi.org/10.1177/1524838000001002005.

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43

Khodaei, Lucas. "Digest: Fossils, evolutionary models, and diatoms*." Evolution 74, no. 1 (December 3, 2019): 210–11. http://dx.doi.org/10.1111/evo.13869.

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44

Bradley, Robert K., and Ian Holmes. "Evolutionary Triplet Models of Structured RNA." PLoS Computational Biology 5, no. 8 (August 28, 2009): e1000483. http://dx.doi.org/10.1371/journal.pcbi.1000483.

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45

Cerviño, M., and V. Luridiana. "Confidence limits of evolutionary synthesis models." Astronomy & Astrophysics 451, no. 2 (May 2006): 475–98. http://dx.doi.org/10.1051/0004-6361:20053283.

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46

Gu, Zhenglong, and Stephen Oliver. "Yeasts as models in evolutionary biology." Genome Biology 10, no. 3 (2009): 304. http://dx.doi.org/10.1186/gb-2009-10-3-304.

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47

Hendrick, Clyde. "Evolutionary Psychology and Models of Explanation." Psychological Inquiry 6, no. 1 (January 1995): 47–49. http://dx.doi.org/10.1207/s15327965pli0601_7.

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48

Vitzthum, Virginia J. "Evolutionary Models of Women's Reproductive Functioning." Annual Review of Anthropology 37, no. 1 (October 2008): 53–73. http://dx.doi.org/10.1146/annurev.anthro.37.081407.085112.

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49

Albertson, Craig, William Cresko, William Detrich, and John Postlethwait. "Evolutionary mutant models for human disease." Developmental Biology 319, no. 2 (July 2008): 493. http://dx.doi.org/10.1016/j.ydbio.2008.05.092.

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50

Mealey, Linda. "Evolutionary models of female intrasexual competition." Behavioral and Brain Sciences 22, no. 2 (April 1999): 234. http://dx.doi.org/10.1017/s0140525x99451817.

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Female competition generally takes nonviolent form, but includes intense verbal and nonverbal harassment that has profound social and physiological consequences. The evolutionary ecological model of competitive reproductive suppression in human females, might profitably be applied to explain a range of contemporary phenomena, including anorexia.

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