Survival rate of model populations depending on the strategy of energy exchange between the organisms
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 20 (2020) no. 2, pp. 241-256.

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The paper addresses the influence of the energy exchange strategy between the organisms of a population in a gradually changing environment on the survival rate of this population. At the first stage of computational experiments, a “boundary” region is determined in the space of two parameters (mutation rate and energy supply rate), within which the survival of populations with zero energy exchange is ambiguous (lies in the interval from 5 to 95%). At the second stage, on the basis of a random sampling of experimental conditions from the boundary region, the dependence of the survival rate of model populations on the fraction of energy transferred during interaction from an organism with larger energy to an organism with smaller one is constructed. The performed experiments demonstrate: 1) the positive effect of altruistic energy exchange (where the organism with larger energy plays the role of a donor) on the survival rate of the populations and 2) the absence of an observable influence of the amount of energy transferred by the parent to the newborn on the survival rate of the populations. The results obtained may be of interest for the construction of artificial populations, for example, in the design of swarms of medical nanorobots or in the development of evolutionary metaheuristic algorithms for solving various optimization problems.
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E. E. Ivanko; S. M. Chervinsky. Survival rate of model populations depending on the strategy of energy exchange between the organisms. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 20 (2020) no. 2, pp. 241-256. http://geodesic.mathdoc.fr/item/ISU_2020_20_2_a9/

[1] Lorenz E. N., “Deterministic nonperiodic flow”, Journal of the Atmospheric Sciences, 20:2 (1963), 130–141 | 2.0.CO;2 class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI | MR | Zbl

[2] Bak P., Tang C., Wiesenfeld K., “Self-organized criticality: An explanation of the 1/f noise”, Phys. Rev. Lett., 59:4 (1987), 381–384 | DOI | MR

[3] May R., “Simple mathematical models with very complicated dynamics”, Nature, 261:5560 (1976), 459–467 | DOI | Zbl

[4] Collier N., “RePast: An extensible framework for agent simulation”, Natural Resources and Environmental Issues, 8 (2001), 4 (accessed 7 March 2019) https://digitalcommons.usu.edu/nrei/vol8/iss1/4

[5] Tisue S., Wilensky U., “NetLogo: A simple environment for modeling complexity”, International Conference on Complex Systems, 21 (2004), 16–21

[6] Luke S., Cioffi-Revilla C., Panait L., Sullivan K., Balan G., “Mason: A multiagent simulation environment”, Simulation, 81:7 (2005), 517–527 | DOI

[7] Trevorrow A., Rokicki T., Hutton T., Greene D., Summers J., Verver M., Golly – a game of life simulator, (accessed 7 March 2019) http://golly.sourceforge.net/

[8] Sayama H., “PyCX: A Python-based simulation code repository for complex systems education”, Complex Adaptive Systems Modeling, 1 (2013), 2 | DOI

[9] Waldrop M. M., Complexity: The Emerging Science at the Edge of Order and Chaos, Simon Schuster, N. Y., 1992, 380 pp.

[10] Sayama H., Introduction to the Modeling and Analysis of Complex Systems, SUNY Binghamton, N. Y., 2015, 478 pp.

[11] Hamann H., Swarm Robotics: A Formal Approach, Springer International Publishing, N. Y., 2018, 210 pp. | DOI

[12] Fitzhugh R., “Impulses and Physiological States in Theoretical Models of Nerve Membrane”, Biophysical Journal, 1:6 (1961), 445–466 | DOI

[13] Drossel B., Schwabl F., “Self-organized criticality in a forest-fire model”, Physica A : Statistical Mechanics and its Applications, 191:1 (1992), 47–50 | DOI

[14] Strogatz S., Sync: The Emerging Science of Spontaneous Order, Penguin, N. Y., 2004, 339 pp. | MR

[15] Wolfram S., A New Kind of Science, Wolfram Media, N. Y., 2002, 1197 pp. | MR | Zbl

[16] Bjorner A., Lovasz L., Shor P. W., “Chip-firing games on graphs”, European Journal of Combinatorics, 12:4 (1991), 283–291 | DOI | MR | Zbl

[17] Clifford P., Sudbury A., “A model for spatial conflict”, Biometrika, 60:3 (1973), 581–588 | DOI | MR | Zbl

[18] Kagel H. J., Roth E. A., The Handbook of Experimental Economics, Princeton Univ. Press, N. J., 1997, 744 pp.

[19] Levin S. A., “Public goods in relation to competition, cooperation, and spite”, PNAS, 111, Supplement 3 (2014), 10838–10845 | DOI

[20] Obolski U., Lewin-Epstein O., Even-Tov E., Ram Y., Hadany L., “With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing”, BMC Evolutionary Biology, 17 (2017), 143 | DOI

[21] Pfeiffer T., Bonhoeffer S., “An evolutionary scenario for the transition to undifferentiated multicellularity”, PNAS, 100:3 (2003), 1095–1098 | DOI

[22] Kreft J.-U., “Biofilms promote altruism”, Microbiology, 150:8 (2004), 2751–2760 | DOI

[23] Cesta A., Miceli M., Rizzo P., “Coexisting agents: Experiments on basic interaction attitude”, Journal of Intelligent Systems, 11:1 (2001), 1–42 | DOI

[24] Ivanko E., “Is evolution always “egolution”: Discussion of evolutionary efficiency of altruistic energy exchange”, Ecological Complexity, 34 (2018), 1–8 | DOI

[25] Hamilton W. D., “The genetical evolution of social behaviour”, Journal of Theoretical Biology, 7:1 (1964), 1–52 | DOI | MR

[26] Trivers R. L., “The evolution of reciprocal altruism”, The Quarterly Review of Biology, 46:1 (1971), 35–57 | DOI

[27] Axelrod R., Hamilton W. D., “The evolution of cooperation”, Science, 211:4489 (1981), 1390–1396 | DOI | MR | Zbl

[28] Nowak M. A., “Five rules for the evolution of cooperation”, Science, 314:5805 (2006), 1560–1563 | DOI | MR

[29] Stuart A., West A., Griffin S., Gardner A., “Evolutionary explanations for cooperation”, Current Biology, 17:16 (2007), R661–R672 | DOI

[30] Lewin-Epstein O., Aharonov R., Hadany L., “Microbes can help explain the evolution of host altruism”, Nature Communications, 8 (2017), 14040 | DOI

[31] Esteban-Fernández de Ávila B., Angsantikul P., Ramírez-Herrera D. E., Soto F., Teymourian H., Dehaini D., Chen Y., Zhang L., Wang J., “Hybrid biomembrane–functionalized nanorobots for concurrent removal of pathogenic bacteria and toxins”, Science Robotics, 3:18 (2018), eaat0485 | DOI

[32] Morice C. P., Kennedy J. J., Rayner N. A., Jones P. D., “Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset”, Journal of Geophysical Research : Atmospheres, 117 (2012), D08101 | DOI

[33] Makeham W. M., “On the Law of Mortality and the Construction of Annuity Tables”, The Assurance Magazine, and Journal of the Institute of Actuaries, 8:6 (1860), 301–310 | DOI

[34] MacArthur R. H., Wilson E. O., The theory of island biogeography, Princeton Univ. Press, N. J., 2001, 224 pp.

[35] Aurenhammer F., Klein R., Lee D.-T., Voronoi Diagrams and Delaunay Triangulations, World Scientific Publishing, N. J., 2013, 348 pp. | MR | Zbl

[36] Uran cluster, (accessed 7 March 2019) http://parallel.uran.ru/node/419

[37] Simon D., Evolutionary Optimization Algorithms, Wiley, N. Y., 2013, 772 pp. | MR | Zbl

[38] Schapire R. E., Freund Y. Y., Boosting: Foundations and Algorithms, The MIT Press, Cambridge, 2012, 544 pp. | MR