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Artículos de revistas sobre el tema "Evolutionary problems"

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Publicado: 10 de marzo de 2023

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1

Dmitriev, V. I. "Solving evolutionary inverse problems". Computational Mathematics and Modeling 22, n.º 3 (julio de 2011): 342–46. http://dx.doi.org/10.1007/s10598-011-9105-y.

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2

Preston, Mark D., Jonathan W. Pitchford y A. Jamie Wood. "Evolutionary optimality in stochastic search problems". Journal of The Royal Society Interface 7, n.º 50 (24 de marzo de 2010): 1301–10. http://dx.doi.org/10.1098/rsif.2010.0090.

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‘Optimal’ behaviour in a biological system is not simply that which maximizes a mean, or temporally and spatially averaged, fitness function. Rather, population dynamics and demographic and environmental stochasticity are fundamental evolutionary ingredients. Here, we revisit the problem of optimal foraging, where some recent studies claim that organisms should forage according to Lévy walks. We show that, in an ecological scenario dominated by uncertainty and high mortality, Lévy walks can indeed be evolutionarily favourable. However, this conclusion is dependent on the definition of efficiency and the details of the simulations. We analyse measures of efficiency that incorporate population-level characteristics, such as variance, superdiffusivity and heavy tails, and compare the results with those generated by simple maximizing of the average encounter rate. These results have implications on stochastic search problems in general, and also on computational models of evolutionary optima.
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3

Karpov, V. E. "Methodological problems in evolutionary computation". Scientific and Technical Information Processing 40, n.º 5 (diciembre de 2013): 286–91. http://dx.doi.org/10.3103/s0147688213050031.

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4

Vishnevskii, M. P. y V. I. Priimenko. "Evolutionary Problems of Nonlinear Magnetoelasticity". Siberian Mathematical Journal 61, n.º 1 (enero de 2020): 47–61. http://dx.doi.org/10.1134/s0037446620010048.

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5

Harvey, Paul H. "Parasitological teeth for evolutionary problems". Nature 342, n.º 6247 (noviembre de 1989): 230. http://dx.doi.org/10.1038/342230a0.

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6

Picard, Rainer, Sascha Trostorff, Marcus Waurick y Maria Wehowski. "On non-autonomous evolutionary problems". Journal of Evolution Equations 13, n.º 4 (15 de septiembre de 2013): 751–76. http://dx.doi.org/10.1007/s00028-013-0201-7.

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7

Baker, Christopher T. H., Kevin Burrage y Neville J. Ford. "Special issue on evolutionary problems". Journal of Computational and Applied Mathematics 205, n.º 2 (agosto de 2007): 667–68. http://dx.doi.org/10.1016/j.cam.2006.06.007.

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8

Haccou, Patsy y Wim J. van der Steen. "Methodological problems in evolutionary biology". Acta Biotheoretica 40, n.º 4 (diciembre de 1992): 285–95. http://dx.doi.org/10.1007/bf00046328.

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9

Bittnar, Zdenek y Barry H. V. Topping. "Evolutionary optimization of engineering problems". Advances in Engineering Software 36, n.º 1 (enero de 2005): 1–2. http://dx.doi.org/10.1016/j.advengsoft.2004.09.002.

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10

Nerukh, A. G. "Evolutionary approach in transient electrodynamics problems". Radio Science 30, n.º 3 (mayo de 1995): 481–91. http://dx.doi.org/10.1029/94rs03377.

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11

Kružík, Martin y Johannes Zimmer. "Evolutionary problems in non-reflexive spaces". ESAIM: Control, Optimisation and Calculus of Variations 16, n.º 1 (21 de octubre de 2008): 1–22. http://dx.doi.org/10.1051/cocv:2008060.

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12

KORDA, V. YU, S. V. BEREZOVSKY, A. S. MOLEV, V. F. KLEPIKOV y L. P. KORDA. "SOLVING VARIATIONAL PROBLEMS VIA EVOLUTIONARY ALGORITHM". International Journal of Modern Physics C 24, n.º 03 (marzo de 2013): 1350009. http://dx.doi.org/10.1142/s0129183113500095.

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We present the evolutionary algorithm that evolves the population of numerical solutions of the variational problem. The evolved solutions are model-independent, smooth, can have a predefined shape (if needed), and satisfy boundary or any other additional conditions (if imposed). To exemplify the performance of the proposed algorithm, we show how to solve the variational problem of searching for the spatially modulated distribution of the field of order parameter that gives a minimum to the Landau-type thermodynamic potential in the theory of ferroelectrics with incommensurate phases.
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13

Liao, Yen-Far, Dun-Han Yau y Chieh-Li Chen. "Evolutionary algorithm to traveling salesman problems". Computers & Mathematics with Applications 64, n.º 5 (septiembre de 2012): 788–97. http://dx.doi.org/10.1016/j.camwa.2011.12.018.

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14

Xie, Y. M. y G. P. Steven. "Evolutionary structural optimization for dynamic problems". Computers & Structures 58, n.º 6 (marzo de 1996): 1067–73. http://dx.doi.org/10.1016/0045-7949(95)00235-9.

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15

Reichel, Joachim y Martin Skutella. "Evolutionary Algorithms and Matroid Optimization Problems". Algorithmica 57, n.º 1 (2 de diciembre de 2008): 187–206. http://dx.doi.org/10.1007/s00453-008-9253-4.

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16

Cao, Yi Jia. "Eigenvalue optimisation problems via evolutionary programming". Electronics Letters 33, n.º 7 (1997): 642. http://dx.doi.org/10.1049/el:19970420.

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17

Emelyanova, Larisa, Svetlana Konovalova, Irina Zubatkina y Margarita Savina. "Evolutionary approach to problems of medicine". Biochimica et Biophysica Acta (BBA) - Bioenergetics 1797 (julio de 2010): 124. http://dx.doi.org/10.1016/j.bbabio.2010.04.371.

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18

Michalewicz, Zbigniew, Dipankar Dasgupta, Rodolphe G. Le Riche y Marc Schoenauer. "Evolutionary algorithms for constrained engineering problems". Computers & Industrial Engineering 30, n.º 4 (septiembre de 1996): 851–70. http://dx.doi.org/10.1016/0360-8352(96)00037-x.

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19

Liu, Zhenhai, Shengda Zeng y Dumitru Motreanu. "Evolutionary problems driven by variational inequalities". Journal of Differential Equations 260, n.º 9 (mayo de 2016): 6787–99. http://dx.doi.org/10.1016/j.jde.2016.01.012.

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20

van der Steen, Wim J. "Methodological problems in evolutionary biology VI. The force of evolutionary epistemology". Acta Biotheoretica 35, n.º 3 (septiembre de 1986): 193–204. http://dx.doi.org/10.1007/bf00052600.

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21

ALI, Fathelalem F., Zensho NAKAO y Richard B. TAN. "An Evolutionary Algorithm for Graph Coloring Problems". Journal of Japan Society for Fuzzy Theory and Systems 12, n.º 5 (2000): 644–53. http://dx.doi.org/10.3156/jfuzzy.12.5_52.

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22

Borgulya, Istv�n. "An Evolutionary Framework for 3-SAT Problems". Journal of Computing and Information Technology 11, n.º 3 (2003): 185. http://dx.doi.org/10.2498/cit.2003.03.06.

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23

Ichikawa, Mitsuo. "From evolutionary studies to global environmental problems". Before Farming 2004, n.º 4 (enero de 2004): 1–5. http://dx.doi.org/10.3828/bfarm.2004.4.7.

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24

Hussein, Balasim Mohammed. "Evolutionary algorithm solution for economic dispatch problems". International Journal of Electrical and Computer Engineering (IJECE) 12, n.º 3 (1 de junio de 2022): 2963. http://dx.doi.org/10.11591/ijece.v12i3.pp2963-2970.

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<span>A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints; these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed; the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.</span>
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25

Huixiang, Zhen, Gong Wenyin y Wang Ling. "Data-driven evolutionary sampling optimization forexpensive problems". Journal of Systems Engineering and Electronics 32, n.º 2 (abril de 2021): 318–30. http://dx.doi.org/10.23919/jsee.2021.000027.

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26

Jong-Hwan Kim y Hyun Myung. "Evolutionary programming techniques for constrained optimization problems". IEEE Transactions on Evolutionary Computation 1, n.º 2 (julio de 1997): 129–40. http://dx.doi.org/10.1109/4235.687880.

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27

Michalewicz, Zbigniew y Marc Schoenauer. "Evolutionary Algorithms for Constrained Parameter Optimization Problems". Evolutionary Computation 4, n.º 1 (marzo de 1996): 1–32. http://dx.doi.org/10.1162/evco.1996.4.1.1.

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Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.
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28

Pantoja, Mario Fernndez, Amelia Rubio Bretones y Rafael Gmez Martin. "Benchmark Antenna Problems for Evolutionary Optimization Algorithms". IEEE Transactions on Antennas and Propagation 55, n.º 4 (abril de 2007): 1111–21. http://dx.doi.org/10.1109/tap.2007.893396.

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29

Qian, Xiaoxiao, Cheng Tang, Yuki Todo, Qiuzhen Lin y Junkai Ji. "Evolutionary Dendritic Neural Model for Classification Problems". Complexity 2020 (19 de agosto de 2020): 1–13. http://dx.doi.org/10.1155/2020/6296209.

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In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) trained by the backpropagation (BP) algorithm, the proposed EDNM is trained by a metaheuristic cuckoo search (CS) algorithm instead, which has been regarded as a global searching algorithm. CS algorithm enables EDNM to avoid several disadvantages, such as slow convergence, trapping into local minimum, and being sensitive to initial values. To evaluate the performance of EDNM, we compare it with a multilayer perceptron (MLP) and CDNM on two benchmark classification problems. The experimental results demonstrate that EDNM is superior to MLP and CDNM in terms of accuracy rate, receiver operator characteristic curve (ROC), and convergence speed. In addition, the neural structure of EDNM can be replaced by a logical circuit completely, which can be implemented in hardware easily. The corresponding experimental results also verify the effectiveness of the logical circuit classifier.
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30

Li, Kangshun y Wei Li. "Hybrid evolutionary algorithm for solving optimization problems". International Journal of Computer Mathematics 84, n.º 11 (noviembre de 2007): 1591–602. http://dx.doi.org/10.1080/00207160601178323.

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31

Gan, Buntara S., Takahiro Hara, Aylie Han, Sofia W. Alisjahbana y Sholihin As’ad. "Evolutionary ACO Algorithms for Truss Optimization Problems". Procedia Engineering 171 (2017): 1100–1107. http://dx.doi.org/10.1016/j.proeng.2017.01.467.

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32

Faella, Luisa, Sara Monsurrò y Carmen Perugia. "Exact controllability for evolutionary imperfect transmission problems". Journal de Mathématiques Pures et Appliquées 122 (febrero de 2019): 235–71. http://dx.doi.org/10.1016/j.matpur.2017.11.011.

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33

Omata, Seiro, Masaki Kazama y Hideaki Nakagawa. "Variational approach to evolutionary free boundary problems". Nonlinear Analysis: Theory, Methods & Applications 71, n.º 12 (diciembre de 2009): e1547-e1552. http://dx.doi.org/10.1016/j.na.2009.01.231.

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34

da Silveira, Luciano Reis, Ricardo Tanscheit y Marley M. B. R. Vellasco. "Quantum inspired evolutionary algorithm for ordering problems". Expert Systems with Applications 67 (enero de 2017): 71–83. http://dx.doi.org/10.1016/j.eswa.2016.08.067.

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35

Conway, Bruce A., Christian M. Chilan y Bradley J. Wall. "Evolutionary principles applied to mission planning problems". Celestial Mechanics and Dynamical Astronomy 97, n.º 2 (8 de diciembre de 2006): 73–86. http://dx.doi.org/10.1007/s10569-006-9052-7.

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36

Park, Seungbae. "Problems with Using Evolutionary Theory in Philosophy". Axiomathes 27, n.º 3 (30 de agosto de 2016): 321–32. http://dx.doi.org/10.1007/s10516-016-9308-0.

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37

Lacomme, Philippe, Christian Prins y Wahiba Ramdane-Chérif. "Evolutionary algorithms for periodic arc routing problems". European Journal of Operational Research 165, n.º 2 (septiembre de 2005): 535–53. http://dx.doi.org/10.1016/j.ejor.2004.04.021.

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38

Malaguti, Enrico y Paolo Toth. "An evolutionary approach for bandwidth multicoloring problems". European Journal of Operational Research 189, n.º 3 (septiembre de 2008): 638–51. http://dx.doi.org/10.1016/j.ejor.2006.09.095.

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39

Fogel, D. B. "Applying evolutionary programming to selected control problems". Computers & Mathematics with Applications 27, n.º 11 (julio de 1994): 89–104. http://dx.doi.org/10.1016/0898-1221(94)90100-7.

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40

Michalewicz, Z. "Evolutionary computation techniques for nonlinear programming problems". International Transactions in Operational Research 1, n.º 2 (abril de 1994): 223–40. http://dx.doi.org/10.1016/0969-6016(94)90022-1.

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41

Ross, L. y B. B. Normark. "Evolutionary problems in centrosome and centriole biology". Journal of Evolutionary Biology 28, n.º 5 (mayo de 2015): 995–1004. http://dx.doi.org/10.1111/jeb.12620.

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42

Köksalan, Murat y Banu Soylu. "Bicriteriap-Hub Location Problems and Evolutionary Algorithms". INFORMS Journal on Computing 22, n.º 4 (noviembre de 2010): 528–42. http://dx.doi.org/10.1287/ijoc.1090.0370.

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43

Myung, Hyun y Jong-Hwan Kim. "Hybrid evolutionary programming for heavily constrained problems". Biosystems 38, n.º 1 (enero de 1996): 29–43. http://dx.doi.org/10.1016/0303-2647(95)01564-7.

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44

Jiménez, F., J. M. Cadenas, J. L. Verdegay y G. Sánchez. "Solving fuzzy optimization problems by evolutionary algorithms". Information Sciences 152 (junio de 2003): 303–11. http://dx.doi.org/10.1016/s0020-0255(03)00074-4.

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45

Trostorff, Sascha. "Exponential stability for second order evolutionary problems". Journal of Mathematical Analysis and Applications 429, n.º 2 (septiembre de 2015): 1007–32. http://dx.doi.org/10.1016/j.jmaa.2015.04.046.

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46

Michalewicz, Zbigniew. "Evolutionary Computation Techniques for Nonlinear Programming Problems". International Transactions in Operational Research 1, n.º 2 (abril de 1994): 223–40. http://dx.doi.org/10.1111/1475-3995.d01-23.

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47

Krzysztof, Wrobel, Torba Pawel, Paszynski Maciej y Byrski Aleksander. "Evolutionary Multi-Agent Computing in Inverse Problems". Computer Science 14, n.º 3 (2013): 367. http://dx.doi.org/10.7494/csci.2013.14.3.367.

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48

Ferguson, K. G. "Semantic and Structural Problems in Evolutionary Ethics". Biology & Philosophy 16, n.º 1 (enero de 2001): 69–84. http://dx.doi.org/10.1023/a:1006739117848.

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49

Xiao, Ningchuan. "An Evolutionary Algorithm for Site Search Problems". Geographical Analysis 38, n.º 3 (julio de 2006): 227–47. http://dx.doi.org/10.1111/j.1538-4632.2006.00684.x.

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50

Pyatkov, S. G. y B. N. Tsybikov. "On evolutionary inverse problems for parabolic equations". Doklady Mathematics 77, n.º 1 (febrero de 2008): 111–13. http://dx.doi.org/10.1134/s1064562408010274.

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