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Journal articles on the topic 'Evolutionary computation applications'

Author: Grafiati

Published: 16 July 2024

Last updated: 31 July 2025

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1

Hong, Tzung-Pei, Chuan-Kang Ting, and Oliver Kramer. "Theory and Applications of Evolutionary Computation." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–2. http://dx.doi.org/10.1155/2010/360796.

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2

Easson, Greg, and H. G. Momm. "Evolutionary Computation for Remote Sensing Applications." Geography Compass 4, no. 3 (2010): 172–92. http://dx.doi.org/10.1111/j.1749-8198.2009.00309.x.

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3

Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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4

Cicirello, Vincent A. "Evolutionary Computation: Theories, Techniques, and Applications." Applied Sciences 14, no. 6 (2024): 2542. http://dx.doi.org/10.3390/app14062542.

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5

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 stor

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6

de Nobel, Jacob. "Conference Overview: Evolutionary Computation Theory and Applications (ECTA) 2024." ACM SIGEVOlution 18, no. 1 (2025): 1–3. https://doi.org/10.1145/3733097.3733099.

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Last year's Evolutionary Computation Theory and Applications (ECTA 2024) conference took place in the beautiful city of Porto, Portugal, from November 20th to 22nd, 2024, as part of the International Joint Conference on Computational Intelligence (IJCCI). The event, organized by INSTICC and supported by ACM SIGAI, AAAI, and other esteemed institutions, brought together researchers worldwide to discuss their latest advancements.

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7

Yar, Morteza Husainy, Vahid Rahmati, and Hamid Reza Dalili Oskouei. "A Survey on Evolutionary Computation: Methods and Their Applications in Engineering." Modern Applied Science 10, no. 11 (2016): 131. http://dx.doi.org/10.5539/mas.v10n11p131.

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Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally catego

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8

Zhang, Biaobiao, Yue Wu, Jiabin Lu, and K. L. Du. "Evolutionary Computation and Its Applications in Neural and Fuzzy Systems." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/938240.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the univers

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9

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

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10

Holmes, John H. "Methods and applications of evolutionary computation in biomedicine." Journal of Biomedical Informatics 49 (June 2014): 11–15. http://dx.doi.org/10.1016/j.jbi.2014.05.008.

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11

CHOPARD, BASTIEN, OLIVIER PICTET, and MARCO TOMASSINP. "PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS." Parallel Algorithms and Applications 15, no. 1-2 (2000): 15–36. http://dx.doi.org/10.1080/01495730008947348.

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12

Kim, Yong-Hyuk, Ahmed Kattan, Michael Kampouridis, and Yourim Yoon. "Discrete Dynamics in Evolutionary Computation and Its Applications." Discrete Dynamics in Nature and Society 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/6043597.

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13

Wang, Youbing, Hao Sun, and Lishan Kang. "The applications of evolutionary computation in software reliability." Wuhan University Journal of Natural Sciences 1, no. 3-4 (1996): 645–50. http://dx.doi.org/10.1007/bf02900900.

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14

Wang, Fei, Hanghang Tong, and Ching-Yung Lin. "Towards Evolutionary Nonnegative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 501–6. http://dx.doi.org/10.1609/aaai.v25i1.7927.

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Nonnegative Matrix Factorization (NMF) techniques has aroused considerable interests from the field of artificial intelligence in recent years because of its good interpretability and computational efficiency. However, in many real world applications, the data features usually evolve over time smoothly. In this case, it would be very expensive in both computation and storage to rerun the whole NMF procedure after each time when the data feature changing. In this paper, we propose Evolutionary Nonnegative Matrix Factorization (eNMF), which aims to incrementally update the factorized matrices in

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15

TAMAKI, Hisashi. "Emergent System and its Applications. Evolutionary Computation and Optimization." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1439–42. http://dx.doi.org/10.2493/jjspe.64.1439.

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16

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

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17

Li, Jian-Yu, Zhi-Hui Zhan, and Jun Zhang. "Evolutionary Computation for Expensive Optimization: A Survey." Machine Intelligence Research 19, no. 1 (2022): 3–23. http://dx.doi.org/10.1007/s11633-022-1317-4.

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AbstractExpensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization

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18

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

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19

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

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20

Madar, Janos, Janos Abonyi, and Ferenc Szeifert. "Interactive evolutionary computation in process engineering." Computers & Chemical Engineering 29, no. 7 (2005): 1591–97. http://dx.doi.org/10.1016/j.compchemeng.2004.12.009.

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21

BENTLEY, PETER. "Special Section: Evolutionary Design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 5 (1999): 325. http://dx.doi.org/10.1017/s0890060499135005.

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This issue of AIEDAM is the second in a series of three “mini” special issues on Evolutionary Design by computers. The papers continue the theme that began in Vol. 13, No. 3, 1999, of using Evolutionary Computation for design problems. The first paper by Eby, Averill, Punch and Goodman provides an excellent overview of the most recent work at Michigan State University on this subject. They describe their work on the optimization of flywheels by an injection island genetic algorithm, and show the importance of minimizing the computation time devoted to evaluation for such real-world application

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22

Skobtsov, Yu A. "Modern Immunological Models and Their Applications." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 3 (140) (September 2022): 61–77. http://dx.doi.org/10.18698/0236-3933-2022-3-61-77.

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he paper considers main models and algorithms of artificial immune systems, which are related to the evolutionary computation paradigm and used to search for potential solutions, each of which is represented by an artificial lymphocyte. Same as an individual in evolutionary computation, an artificial lymphocyte is most often encoded by a binary string or a vector of real numbers. As far as the main models of artificial immune systems are concerned, the clonal selection algorithm is close to the evolutionary strategy of evolutionary computing, though it uses more powerful mutation operators and

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23

Liu, Feng, Hanyang Wang, Jiahao Zhang, et al. "EvoGAN: An evolutionary computation assisted GAN." Neurocomputing 469 (January 2022): 81–90. http://dx.doi.org/10.1016/j.neucom.2021.10.060.

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24

Kwong, Sam, and Qingfu Zhang. "Bridging machine learning and evolutionary computation." Neurocomputing 146 (December 2014): 1. http://dx.doi.org/10.1016/j.neucom.2014.06.051.

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25

Martínez-Torres, M. R., and S. L. Toral-Marín. "Strategic group identification using evolutionary computation." Expert Systems with Applications 37, no. 7 (2010): 4948–54. http://dx.doi.org/10.1016/j.eswa.2009.12.019.

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26

Jakobovic, Domagoj, Marko Đurasević, Stjepan Picek, and Bruno Gašperov. "ECF: A C＋＋ framework for evolutionary computation." SoftwareX 27 (September 2024): 101640. http://dx.doi.org/10.1016/j.softx.2024.101640.

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27

Yao, Xin, and Yong Xu. "Recent Advances in Evolutionary Computation." Journal of Computer Science and Technology 21, no. 1 (2006): 1–18. http://dx.doi.org/10.1007/s11390-006-0001-4.

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28

Naji, Dr Loma Shafiq MOHD. "The Impact of Artificial Intelligence Applications on the Digital Marketing Development on the Telecommunications Companies in Jordan." Webology 19, no. 1 (2022): 854–66. http://dx.doi.org/10.14704/web/v19i1/web19059.

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This research mainly aims to analyze the main effect of the Artificial Intelligence applications; neural networks, expert systems and evolutionary computation, on digital marketing development. Specifically, the aim is to evaluate the new artificial intelligence applications in Jordan, especially those which can affect digital marketing development. Besides, the researcher designed questionnaires which were given on the basis of a technique of simple sampling. Moreover, they were applied on the Jordanian telecommunications companies. A number of 375 questionnaires were distributed. Furthermore

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29

Wong, Kit Po, and Zhao Yang Dong. "Special issue on evolutionary computation for systems and control applications." International Journal of Systems Science 35, no. 13-14 (2004): 729–30. http://dx.doi.org/10.1080/00207720412331303615.

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30

KOBAYASHI, Shigenobu. "Emergent System and its Applications. System Synthesis by Evolutionary Computation." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1419–22. http://dx.doi.org/10.2493/jjspe.64.1419.

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31

Al-Muhaideb, Sarab, and Mohamed El Bachir Menai. "Evolutionary computation approaches to the Curriculum Sequencing problem." Natural Computing 10, no. 2 (2011): 891–920. http://dx.doi.org/10.1007/s11047-010-9246-5.

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32

Raymer, Michael L. "Book Review: Evolutionary Computation in Bioinformatics." Genetic Programming and Evolvable Machines 6, no. 2 (2005): 229–30. http://dx.doi.org/10.1007/s10710-005-7581-6.

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33

Ling, Sai Ho, and Hak Keung Lam. "Evolutionary Algorithms in Health Technologies." Algorithms 12, no. 10 (2019): 202. http://dx.doi.org/10.3390/a12100202.

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Health technology research brings together complementary interdisciplinary research skills in the development of innovative health technology applications. Recent research indicates that artificial intelligence can help achieve outstanding performance for particular types of health technology applications. An evolutionary algorithm is one of the subfields of artificial intelligence, and is an effective algorithm for global optimization inspired by biological evolution. With the rapidly growing complexity of design issues, methodologies and a higher demand for quality health technology applicat

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34

Sayama, Hiroki, and Shelley D. Dionne. "Studying Collective Human Decision Making and Creativity with Evolutionary Computation." Artificial Life 21, no. 3 (2015): 379–93. http://dx.doi.org/10.1162/artl_a_00178.

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We report a summary of our interdisciplinary research project “Evolutionary Perspective on Collective Decision Making” that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, w

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35

Mrozek, Adam, Wacław Kuś, and Łukasz Sztangret. "Real-time evolutionary optimization of metallurgical processes using ARM microcontroller." Computer Methods in Material Science 16, no. 1 (2016): 20–26. http://dx.doi.org/10.7494/cmms.2016.1.0556.

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The real time (RT) computations with the use of microcontrollers have been present in everyday life for years. They are very useful in e.g. online control of processes due to the ability to determine the proper control in case of any environment changes. The algorithms employed in RT computation must be as simple as possible to meet the imposed time limits. On the other hand, the continuous increase in computational power of modern microcontrollers and embedded platforms causes that more complex algorithms can be performed in the real time. However, during implementation of any algorithm the s

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36

SARKER, RUHUL, JOARDER KAMRUZZAMAN, and CHARLES NEWTON. "EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS." International Journal of Computational Intelligence and Applications 03, no. 04 (2003): 311–30. http://dx.doi.org/10.1142/s1469026803001051.

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Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also i

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37

Shilpa, V., and V. Shetty Suma. "Design of Systolic Architecture Using Evolutionary Computation." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2815–20. https://doi.org/10.31142/ijtsrd15776.

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This work presents a new concept for finding the optimal values for the entire three fundamental design vectors namely scheduling, projection and processor so that not only architecture design could be feasible along with that maximum hardware utilizing efficiency could be achieved. This Approach also having the focus to minimize the total delay involved with systolic architecture design. Evolutionary programming has applied to find the optimal solution. Presented work and result will provide facility to designer without any involvement to find out best suited architecture for a particular app

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38

Wakabayashi, Kenichi, and Masayuki Yamamura. "A Design for Cellular Evolutionary Computation by using Bacteria." Natural Computing 4, no. 3 (2005): 275–92. http://dx.doi.org/10.1007/s11047-004-5236-9.

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39

Fernández de Vega, Francisco, Gustavo Olague, Leonardo Trujillo, and Daniel Lombraña González. "Customizable execution environments for evolutionary computation using BOINC + virtualization." Natural Computing 12, no. 2 (2012): 163–77. http://dx.doi.org/10.1007/s11047-012-9343-8.

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40

Loughran, Róisín, and Michael O’Neill. "Evolutionary music: applying evolutionary computation to the art of creating music." Genetic Programming and Evolvable Machines 21, no. 1-2 (2020): 55–85. http://dx.doi.org/10.1007/s10710-020-09380-7.

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41

ONO, Isao. "Emergent System and its Applications. Applying Evolutionary Computation to Lens Design." Journal of the Japan Society for Precision Engineering 64, no. 10 (1998): 1443–46. http://dx.doi.org/10.2493/jjspe.64.1443.

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42

Myyara, Marouane, Oussama Lagnfdi, Anouar Darif, and Abderrazak Farchane. "A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4186. http://dx.doi.org/10.11591/ijai.v13.i4.pp4186-4194.

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The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel gene

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43

Marouane, Myyara, Lagnfdi Oussama, Darif Anouar, and Farchane Abderrazak. "A new approach based on genetic algorithm for computation offloading optimization in multi-access edge computing networks." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4186–94. https://doi.org/10.11591/ijai.v13.i4.pp4186-4194.

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The proliferation of smart devices and the increasing demand for resource intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, optimizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic

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44

Divina, F., and J. S. Aguilar-Ruiz. "Biclustering of expression data with evolutionary computation." IEEE Transactions on Knowledge and Data Engineering 18, no. 5 (2006): 590–602. http://dx.doi.org/10.1109/tkde.2006.74.

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45

Tauritz, D. R., J. N. Kok, and I. G. Sprinkhuizen-Kuyper. "Adaptive Information Filtering using evolutionary computation." Information Sciences 122, no. 2-4 (2000): 121–40. http://dx.doi.org/10.1016/s0020-0255(99)00123-1.

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46

Mashwani, Wali Khan, Zia Ur Rehman, Maharani A. Bakar, Ismail Koçak, and Muhammad Fayaz. "A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems." Complexity 2021 (March 10, 2021): 1–24. http://dx.doi.org/10.1155/2021/5515701.

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Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of

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47

Roy, Ranjit, Praghnesh Bhatt, and S. P. Ghoshal. "Evolutionary computation based three-area automatic generation control." Expert Systems with Applications 37, no. 8 (2010): 5913–24. http://dx.doi.org/10.1016/j.eswa.2010.02.014.

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48

Lee, Wei-Po. "Parallelizing evolutionary computation: A mobile agent-based approach." Expert Systems with Applications 32, no. 2 (2007): 318–28. http://dx.doi.org/10.1016/j.eswa.2005.11.034.

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49

Ritu Jain, Anjali Dadhich, Roshni Rajput, Dinesh Singh Dhakar,. "Investigating the Role of Machine Leaning and Blockchain for Forecasting Stock Market Trends." Journal of Electrical Systems 20, no. 2s (2024): 721–29. http://dx.doi.org/10.52783/jes.1569.

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There is a sudden deluge of evolutionary approaches in the domain of data processing and computation, which are significantly affecting several facets of applications globally. Artificial intelligence, machine learning and Blockchain happen to be at the forefront of evolutionary computation beating conventional approaches. Finance applications currently are heavily reliant of data driven models. Stock trend analysis happens to be one such approach, which lays the foundation for forecasting decisions to be made. The leeway in such applications is critically small as minimal inaccuracies in fore

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50

Zhao, Tian-Fang, Wei-Neng Chen, Xin-Xin Ma, and Xiao-Kun Wu. "Evolutionary Computation in Social Propagation over Complex Networks: A Survey." International Journal of Automation and Computing 18, no. 4 (2021): 503–20. http://dx.doi.org/10.1007/s11633-021-1302-3.

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AbstractSocial propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is tha

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