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Relevant bibliographies by topics / Evolutionary computation / Journal articles

Journal articles on the topic 'Evolutionary computation'

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

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Foster, James A. "Evolutionary computation." Nature Reviews Genetics 2, no. 6 (June 2001): 428–36. http://dx.doi.org/10.1038/35076523.

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2

Jong, Kenneth De. "Evolutionary computation." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 52–56. http://dx.doi.org/10.1002/wics.5.

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3

Kozlov, AP. "Biological Computation and Compatibility Search in the Possibility Space as the Mechanism of Complexity Increase During Progressive Evolution." Evolutionary Bioinformatics 18 (January 2022): 117693432211106. http://dx.doi.org/10.1177/11769343221110654.

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The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes.

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4

V, Shilpa, and Suma V. Shetty. "Design of Systolic Architecture Using Evolutionary Computation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2815–20. http://dx.doi.org/10.31142/ijtsrd15776.

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5

Bush, Benjamin James, and Hiroki Sayama. "Hyperinteractive Evolutionary Computation." IEEE Transactions on Evolutionary Computation 15, no. 3 (June 2011): 424–33. http://dx.doi.org/10.1109/tevc.2010.2096539.

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6

Takagi, Hideyuki, and Hitoshi Iba. "Interactive evolutionary computation." New Generation Computing 23, no. 2 (June 2005): 113–14. http://dx.doi.org/10.1007/bf03037488.

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7

Ivkovic, Nikola, Domagoj Jakobovic, and Marin Golub. "Measuring Performance of Optimization Algorithms in Evolutionary Computation." International Journal of Machine Learning and Computing 6, no. 3 (June 2016): 167–71. http://dx.doi.org/10.18178/ijmlc.2016.6.3.593.

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8

Brunello, Andrea, Enrico Marzano, Angelo Montanari, and Guido Sciavicco. "Decision Tree Pruning via Multi-Objective Evolutionary Computation." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 167–75. http://dx.doi.org/10.18178/ijmlc.2017.7.6.641.

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9

TAMAKI, Hisashi. "Evolutionary Computation and Optimization." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 4 (1998): 593–601. http://dx.doi.org/10.3156/jfuzzy.10.4_13.

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10

Yasuda, Keiichiro. "Evolutionary Computation and Metaheuristics." IEEJ Transactions on Electronics, Information and Systems 122, no. 3 (2002): 320–23. http://dx.doi.org/10.1541/ieejeiss1987.122.3_320.

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11

López-ibáñez, Manuel, Juergen Branke, and Luís Paquete. "Reproducibility in Evolutionary Computation." ACM Transactions on Evolutionary Learning and Optimization 1, no. 4 (December 31, 2021): 1–21. http://dx.doi.org/10.1145/3466624.

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Experimental studies are prevalent in Evolutionary Computation ( EC ), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery ( ACM ) adopted by ACM Transactions on Evolutionary Learning and Optimization ( TELO ). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

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12

Lucas, S. M., and G. Kendall. "Evolutionary computation and games." IEEE Computational Intelligence Magazine 1, no. 1 (February 2006): 10–18. http://dx.doi.org/10.1109/mci.2006.1597057.

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13

Mitchell, Melanie, and Charles E. Taylor. "Evolutionary Computation: An Overview." Annual Review of Ecology and Systematics 30, no. 1 (November 1999): 593–616. http://dx.doi.org/10.1146/annurev.ecolsys.30.1.593.

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14

De Jong, Kenneth A. "Evolutionary computation for discovery." Communications of the ACM 42, no. 11 (November 1999): 51–53. http://dx.doi.org/10.1145/319382.319392.

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15

Friedrich, Tobias, and Jonathan Rowe. "Genetic and Evolutionary Computation." Theoretical Computer Science 545 (August 2014): 1. http://dx.doi.org/10.1016/j.tcs.2014.07.002.

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16

Friedrich, Tobias, Jun He, Thomas Jansen, and Alberto Moraglio. "Genetic and Evolutionary Computation." Theoretical Computer Science 561 (January 2015): 1–2. http://dx.doi.org/10.1016/j.tcs.2014.11.022.

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17

Doerr, Benjamin, and Thomas Jansen. "Theory of Evolutionary Computation." Algorithmica 59, no. 3 (November 9, 2010): 299–300. http://dx.doi.org/10.1007/s00453-010-9472-3.

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18

Sendhoff, Bernhard, Mark Roberts, and Xin Yao. "Evolutionary Computation Benchmarking Repository." IEEE Computational Intelligence Magazine 1, no. 4 (November 2006): 50–53. http://dx.doi.org/10.1109/ci-m.2006.248058.

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19

Fogel, D. B. "What is evolutionary computation?" IEEE Spectrum 37, no. 2 (February 2000): 26–32. http://dx.doi.org/10.1109/6.819926.

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20

Du, Zhenyu. "Special issue on Intelligence Computation Evolutionary Computation: ICEV2018." Evolutionary Intelligence 12, no. 3 (July 31, 2019): 321–22. http://dx.doi.org/10.1007/s12065-019-00271-0.

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21

Bonissone, P. P., R. Subbu, N. Eklund, and T. R. Kiehl. "Evolutionary algorithms + domain knowledge = real-world evolutionary computation." IEEE Transactions on Evolutionary Computation 10, no. 3 (June 2006): 256–80. http://dx.doi.org/10.1109/tevc.2005.857695.

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22

Burgin, M., and E. Eberbach. "Evolutionary Automata: Expressiveness and Convergence of Evolutionary Computation." Computer Journal 55, no. 9 (September 29, 2011): 1023–29. http://dx.doi.org/10.1093/comjnl/bxr099.

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23

Kohata, Naoki, Toru Yamaguchi, Takanobu Baba, and Hideki Hashimoto. "Chaotic Evolutionary Parallel Computation on Intelligent Agents." Journal of Robotics and Mechatronics 10, no. 5 (October 20, 1998): 424–30. http://dx.doi.org/10.20965/jrm.1998.p0424.

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This paper proposes evolutionary computation using chaotic dynamics rather than the conventional genetic algorithm (GA) for such intelligent agents as welfare robots. Proposed evolutionary computation applies chaotic retrieval to associative memory. We applied evolutionary computation to multiagent robots moving side by side in step. Evolutionary computation is basically parallel processing, so we implement its parallel processing algorithm on A-NET (Actors NETwork) parallel objectoriented computer to show usefulness of parallel processing in evolutionary computation.

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24

Karpov, V. E. "Methodological problems in evolutionary computation." Scientific and Technical Information Processing 40, no. 5 (December 2013): 286–91. http://dx.doi.org/10.3103/s0147688213050031.

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25

Vanneschi, Leonardo, Luca Mussi, and Stefano Cagnoni. "Hot topics in Evolutionary Computation." Intelligenza Artificiale 5, no. 1 (2011): 5–17. http://dx.doi.org/10.3233/ia-2011-0001.

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26

Ono, Isao. "An Introduction to Evolutionary Computation." IEEJ Transactions on Electronics, Information and Systems 122, no. 3 (2002): 324–29. http://dx.doi.org/10.1541/ieejeiss1987.122.3_324.

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27

MUNETOMO, Masaharu, and Hiroshi SOMEYA. "How to Apply Evolutionary Computation." Journal of The Institute of Electrical Engineers of Japan 132, no. 4 (2012): 204–7. http://dx.doi.org/10.1541/ieejjournal.132.204.

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28

OYAMA, Akira. "Evolutionary Computation in Space Engineering." Journal of The Institute of Electrical Engineers of Japan 132, no. 4 (2012): 208–11. http://dx.doi.org/10.1541/ieejjournal.132.208.

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29

Aguiar-Pulido, Vanessa, Marcos Gestal, Maykel Cruz-Monteagudo, Juan Rabunal, Julian Dorado, and Cristian Munteanu. "Evolutionary Computation and QSAR Research." Current Computer Aided-Drug Design 9, no. 2 (June 1, 2013): 206–25. http://dx.doi.org/10.2174/1573409911309020006.

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30

Kordon, Arthur. "Evolutionary computation at Dow Chemical." ACM SIGEVOlution 1, no. 3 (September 2006): 4–9. http://dx.doi.org/10.1145/1181964.1181965.

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31

Jin'no, Kenya. "Special issue on evolutionary computation." Nonlinear Theory and Its Applications, IEICE 10, no. 3 (2019): 279. http://dx.doi.org/10.1587/nolta.10.279.

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32

Handa, Hisashi, Hiroshi Kawakami, and Osamu Katai. "Recent Advances in Evolutionary Computation." IEEJ Transactions on Electronics, Information and Systems 128, no. 3 (2008): 334–39. http://dx.doi.org/10.1541/ieejeiss.128.334.

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33

Bhandarkar, S. M., and Hui Zhang. "Image segmentation using evolutionary computation." IEEE Transactions on Evolutionary Computation 3, no. 1 (April 1999): 1–21. http://dx.doi.org/10.1109/4235.752917.

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34

Fogel, D. B. "Evolutionary Computation: A New Transactions." IEEE Transactions on Evolutionary Computation 1, no. 1 (April 1997): 1–2. http://dx.doi.org/10.1109/tevc.1997.585887.

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35

Lim, Dudy, Yaochu Jin, Yew-Soon Ong, and Bernhard Sendhoff. "Generalizing Surrogate-Assisted Evolutionary Computation." IEEE Transactions on Evolutionary Computation 14, no. 3 (June 2010): 329–55. http://dx.doi.org/10.1109/tevc.2009.2027359.

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36

Miikkulainen, Risto, Paul Jarratt, and Andrew Turner. "Evolutionary computation experts video collection." ACM SIGEVOlution 11, no. 3 (November 12, 2018): 6. http://dx.doi.org/10.1145/3292044.3292046.

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37

Hong, Tzung-Pei, Ke-Yuan Huang, and Wen-Yang Lin. "Adversarial Search by Evolutionary Computation." Evolutionary Computation 9, no. 3 (September 2001): 371–85. http://dx.doi.org/10.1162/106365601750406046.

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In this paper, we consider the problem of finding good next moves in two-player games. Traditional search algorithms, such as minimax and α-β pruning, suffer great temporal and spatial expansion when exploring deeply into search trees to find better next moves. The evolution of genetic algorithms with the ability to find global or near global optima in limited time seems promising, but they are inept at finding compound optima, such as the minimax in a game-search tree. We thus propose a new genetic algorithm-based approach that can find a good next move by reserving the board evaluation values of new offspring in a partial game-search tree. Experiments show that solution accuracy and search speed are greatly improved by our algorithm.

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38

Rothlauf, Franz, and David E. Goldberg. "Redundant Representations in Evolutionary Computation." Evolutionary Computation 11, no. 4 (December 2003): 381–415. http://dx.doi.org/10.1162/106365603322519288.

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This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2kr/r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.

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39

Harman, Mark. "Software Engineering Meets Evolutionary Computation." Computer 44, no. 10 (October 2011): 31–39. http://dx.doi.org/10.1109/mc.2011.263.

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40

Mühlenbein, H., and Th Mahnig. "Evolutionary computation and Wright's equation." Theoretical Computer Science 287, no. 1 (September 2002): 145–65. http://dx.doi.org/10.1016/s0304-3975(02)00098-1.

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41

Yao, Xin. "Evolutionary computation comes of age." Cognitive Systems Research 1, no. 1 (December 1999): 59–64. http://dx.doi.org/10.1016/s1389-0417(99)00006-6.

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42

Miranda, Vladimiro, Dipti Srinivasan, and LM Proença. "Evolutionary computation in power systems." International Journal of Electrical Power & Energy Systems 20, no. 2 (February 1998): 89–98. http://dx.doi.org/10.1016/s0142-0615(97)00040-9.

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43

Murawski, K., T. Arciszewski, and K. De Jong. "Evolutionary Computation in Structural Design." Engineering with Computers 16, no. 3-4 (December 2000): 275–86. http://dx.doi.org/10.1007/pl00013716.

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44

Claveria, Oscar, Enric Monte, and Salvador Torra. "Evolutionary Computation for Macroeconomic Forecasting." Computational Economics 53, no. 2 (November 7, 2017): 833–49. http://dx.doi.org/10.1007/s10614-017-9767-4.

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45

Wang, Jing-Zhong, Yingchieh Ho, and Tsung-Ying Sun. "Fuzzy Scaled Mutation Evolutionary Computation." International Journal of Fuzzy Systems 18, no. 6 (February 18, 2016): 1162–79. http://dx.doi.org/10.1007/s40815-016-0155-3.

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46

Reeves, Colin R. "Evolutionary computation: a unified approach." Genetic Programming and Evolvable Machines 8, no. 3 (July 26, 2007): 293–95. http://dx.doi.org/10.1007/s10710-007-9035-9.

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47

Li, Xiaodong, Wenjian Luo, and Xin Yao. "Theoretical foundations of evolutionary computation." Genetic Programming and Evolvable Machines 9, no. 2 (November 10, 2007): 107–8. http://dx.doi.org/10.1007/s10710-007-9047-5.

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48

Bull, L. "On Model-Based Evolutionary Computation." Soft Computing - A Fusion of Foundations, Methodologies and Applications 3, no. 2 (September 23, 1999): 76–82. http://dx.doi.org/10.1007/s005000050055.

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49

Zhang, Qingfu. "Knowledge incorporation in Evolutionary Computation." IEEE Computational Intelligence Magazine 1, no. 4 (November 2006): 58–59. http://dx.doi.org/10.1109/ci-m.2006.248069.

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50

Bhattacharyya, Siddhartha. "Evolutionary computation for database marketing." Journal of Database Marketing & Customer Strategy Management 10, no. 4 (July 2003): 343–52. http://dx.doi.org/10.1057/palgrave.jdm.3240123.

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