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Готові списки джерел за темами / Evolutional computation / Статті в журналах

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Статті в журналах з теми "Evolutional computation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Interactive Reduct Evolutional Computation for Aesthetic Design." Journal of Computing and Information Science in Engineering 5, no. 1 (2005): 1–7. http://dx.doi.org/10.1115/1.1846055.

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We propose a method of evolving designs based on the user’s personal preferences. The method works through an interaction between the user and a computer system. The method’s objective is to help the customer to set design parameters via a simple evaluation of displayed samples. An important feature is that the design attributes to which the user pays more attention (favored features) are estimated using reducts in rough set theory and reflected when refining the design. New design candidates are generated by the user’s evaluation of design samples generated at random. The values of attributes

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2

Yang, Cheng Zhong, and Xiao Shi Zheng. "A Video Characteristics Watermarking Algorithm Based on Bees Evolutional Computation." Applied Mechanics and Materials 571-572 (June 2014): 796–802. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.796.

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Digital video watermarking is an efficient way of copyright protection. A robust watermarking was proposed that ccording to the video features using the evolutional method of video image classification processing, suitable for video performance and human visual characteristics of video watermarking, and put forward a kind of using Bees evolutional algorithm for video image classification processing of video watermarking algorithm. All the key frame numbers are converts to a binary matrix after data transformation. At last the binary image is produced. From this, the watermarking image reflects

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3

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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4

Chen, Jun-Lang, and Chao-Qing Dai. "New Variable Separation Solutions of the (2+1)-Dimensional Generalized Broer-Kaup System." Zeitschrift für Naturforschung A 62, no. 12 (2007): 677–84. http://dx.doi.org/10.1515/zna-2007-1201.

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Using symbolic and algebra computation, the extended tanh-function method (ETM) based on the mapping method is further extended. New variable separation solutions of the (2+1)-dimensional generalized Broer-Kaup (GBK) system are derived. From the periodic wave solution and by selecting appropriate functions, the evolutional behaviours of dromions in the background of Jacobian elliptic wave and their interaction behaviours are investigated.

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5

Anderson, William, and Mohammad Farazmand. "Fast and scalable computation of shape-morphing nonlinear solutions with application to evolutional neural networks." Journal of Computational Physics 498 (February 2024): 112649. http://dx.doi.org/10.1016/j.jcp.2023.112649.

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6

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Design optimisation for customers' KANSEI requirement: application of interactive reduct evolutional computation to industrial design with curves." International Journal of Manufacturing Technology and Management 10, no. 4 (2007): 360. http://dx.doi.org/10.1504/ijmtm.2007.012155.

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7

Niu, Qun, Taijin Zhou, and Shiwei Ma. "A Quantum-Inspired Immune Algorithm for Hybrid Flow Shop with Makespan Criterion." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 765–85. https://doi.org/10.3217/jucs-015-04-0765.

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This paper presents a quantum-inspired immune algorithm (QIA) for Hybrid flow shop problems (HFSP) to minimize makespan. Since HFSP have been proved to be NP-hard in a strong sense when the objective is to minimize the makespan, an effective immune algorithm (IA) is used to solve the problems. IA is a kind of evolutional computation strategies, which is developed on the basis of a real immune mechanism in the human body, and has been employed to tackle complex scheduling problems and produce a reasonable manufacturing schedule. In order to achieve better results, the standard IA is combined wi

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8

MIYAGAWA, Kengo, Jiro SAKAMOTO, Satoshi KITAYAMA, and Nobutaka SHIMIZU. "Optimum design of cross-sectional sizes for lip groove-shaped steel considering compression and bending load by using evolutional computation." Proceedings of OPTIS 2018.13 (2018): 303. http://dx.doi.org/10.1299/jsmeoptis.2018.13.303.

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9

Whitley, L. Darrell, Francisco Chicano, and Brian W. Goldman. "Gray Box Optimization for Mk Landscapes (NK Landscapes and MAX-kSAT)." Evolutionary Computation 24, no. 3 (2016): 491–519. http://dx.doi.org/10.1162/evco_a_00184.

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This article investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in [Formula: see text] time. Bounded separable problems are also solved in [Formula: see text] time. As a result, Gray Box Optimization is able to solve many commonly used problems from the evoluti

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10

Utku, Kose, and Arslan Ahmet. "BRAIN Journal - On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 5, no. 1-4 (2014): 60–66. https://doi.org/10.5281/zenodo.1044111.

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ABSTRACT In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the sol

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11

Dr.K.Lenin. "VORTEX OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM." International Journal of Research - Granthaalayah 6, no. 1 (2018): 266–76. https://doi.org/10.5281/zenodo.1164144.

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In this paper, a new Vortex Optimization (VO) algorithm is proposed to solve the reactive power problem. The idea is generally focused on a typical Vortex flow in nature and enthused from some dynamics that are occurred in the sense of Vortex nature. In a few words, the algorithm is also a swarm-oriented evolutional problem solution methodology; since it comprises numerous techniques related to removal of feeble swarm members and trying to progress the solution procedure by supporting the solution space through fresh swarm members. In order to evaluate the performance of the proposed Vortex Op

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12

Wang, Lin, Xiyu Liu, Jianhua Qu, Yuzhen Zhao, Liang Gao, and Qianqian Ren. "An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering." Applied Sciences 13, no. 13 (2023): 7755. http://dx.doi.org/10.3390/app13137755.

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In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify th

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13

P., Anilkumar, and Venugopal P. "An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images." PLOS ONE 18, no. 10 (2023): e0290624. http://dx.doi.org/10.1371/journal.pone.0290624.

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Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has b

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14

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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15

Larsen, Brett W., and Shaul Druckmann. "Towards a more general understanding of the algorithmic utility of recurrent connections." PLOS Computational Biology 18, no. 6 (2022): e1010227. http://dx.doi.org/10.1371/journal.pcbi.1010227.

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Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation”

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16

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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17

Wilson, Robert A. "What Computations (Still, Still) Can't Do: Jerry Fodor on Computation and Modularity." Canadian Journal of Philosophy Supplementary Volume 30 (2004): 407–25. http://dx.doi.org/10.1080/00455091.2004.10717612.

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Jerry Fodor's The Mind Doesn't Work That Way (2000; hereafter Mind) purports to do a number of things. To name three: First, it aims to show what is problematic about recent evolutionary psychology, especially as popularized in Steven Pinker's How the Mind Works (1997). Fodor's particular target here is the rose-coloured view of evolutionary psychology as offering a “new synthesis” in integrating computational psychology with evolutionary theory. Second, Fodor's book poses a series of related, in-principle problems for any cognitive theory that revolve around the putative tension between the l

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18

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 (2018): 2815–20. http://dx.doi.org/10.31142/ijtsrd15776.

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19

Różycki, Rafał, and Grzegorz Waligóra. "Energy-Aware Evolutionary Algorithm for Scheduling Jobs of Charging Electric Vehicles in an Autonomous Charging Station." Energies 16, no. 18 (2023): 6502. http://dx.doi.org/10.3390/en16186502.

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The paper considers an innovative model of autonomous charging stations where a program implementing a scheduling algorithm and a set of jobs being scheduled are driven by the same common power source. It is assumed that one of the well-known local search metaheuristics—an evolutionary algorithm—is used for the scheduling process. The algorithm is designed to search for a sequence of charging jobs resulting in a schedule of the minimum length. Since processors with variable processing speeds can be used for computations, this has interesting consequences both from a theoretical and practical p

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20

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

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21

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

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22

Khalid, Salman, Waqas Bin Abbas, Hyung Seok Kim, and Muhammad Tabish Niaz. "Evolutionary Algorithm Based Capacity Maximization of 5G/B5G Hybrid Pre-Coding Systems." Sensors 20, no. 18 (2020): 5338. http://dx.doi.org/10.3390/s20185338.

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Hybrid pre-coding strategies are considered as a potential solution for combating path loss experienced by Massive MIMO systems operating at millimeter wave frequencies. The partially connected structure is preferred over the fully connected structure due to smaller computational complexity. In order to improve the spectral efficiency of a partially connected hybrid pre-coding architecture, which is one of the requirements of future 5G/B5G systems, this work proposes the application of evolutionary algorithms for joint computation of RF and the digital pre-coder. The evolutionary algorithm bas

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23

Zhuo, Jun Fei, Xing He Wu, Guan Zhao Wu, and Min Yao. "Research on Methodology of Evolutionary Computing." Advanced Materials Research 490-495 (March 2012): 524–28. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.524.

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Evolutionary computing is one of the important branches in computational intelligence. This paper mainly introduces four new branches of the evolutionary computation, i.e. Gene Expression Programming (GEP), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Estimation of Distribution Algorithms (EDA).

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24

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 (2016): 167–71. http://dx.doi.org/10.18178/ijmlc.2016.6.3.593.

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25

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 (2017): 167–75. http://dx.doi.org/10.18178/ijmlc.2017.7.6.641.

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26

Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "COMPUTATIONAL BIOLOGY." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (2018): 66. http://dx.doi.org/10.23956/ijarcsse.v8i6.616.

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Computation is an integral part of a larger revolution that will affect how science is conducted. Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.

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27

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

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28

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

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29

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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30

Dodig-Crnkovic, Gordana. "Cognition as Morphological/Morphogenetic Embodied Computation In Vivo." Entropy 24, no. 11 (2022): 1576. http://dx.doi.org/10.3390/e24111576.

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Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The pre

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31

Kempes, Christopher P., David Wolpert, Zachary Cohen, and Juan Pérez-Mercader. "The thermodynamic efficiency of computations made in cells across the range of life." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2109 (2017): 20160343. http://dx.doi.org/10.1098/rsta.2016.0343.

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Biological organisms must perform computation as they grow, reproduce and evolve. Moreover, ever since Landauer’s bound was proposed, it has been known that all computation has some thermodynamic cost—and that the same computation can be achieved with greater or smaller thermodynamic cost depending on how it is implemented. Accordingly an important issue concerning the evolution of life is assessing the thermodynamic efficiency of the computations performed by organisms. This issue is interesting both from the perspective of how close life has come to maximally efficient computation (presumabl

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32

Muravyov, Sergey, Denis Antipov, Arina Buzdalova, and Andrey Filchenkov. "Efficient Computation of Fitness Function for Evolutionary Clustering." MENDEL 25, no. 1 (2019): 87–94. http://dx.doi.org/10.13164/mendel.2019.1.087.

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Evolutionary algorithms (EAs) are random search heuristics which can solve various optimization problems. There are plenty of papers describing different approaches developed to apply evolutionary algorithms to the clustering problem, although none of them addressed the problem of fitness function computation. In clustering, many clustering validity indices exist that are designed to evaluate quality of resulting points partition. It is hard to use them as a fitness function due to their computational complexity. In this paper, we propose an efficient method for iterative computation of cluste

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33

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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34

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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Анотація:

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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35

Wagner, Hermann, and Dirk Kautz. "Evolutionary conservation and ontogenetic emergence of neural algorithms." Behavioral and Brain Sciences 21, no. 2 (1998): 285–86. http://dx.doi.org/10.1017/s0140525x98521176.

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Neural algorithms are conserved during evolution. Neurons with different shapes and using different molecular mechanisms can perform the same computation. However, evolutionary conservation of neural algorithms is not sufficient for claiming the realization of an algorithm for a specific computational problem. A plausible scheme for ontogenetic emergence of the structure of the algorithm must also be provided.

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36

Hu, Ting, and Wolfgang Banzhaf. "Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology." Journal of Artificial Evolution and Applications 2010 (June 2, 2010): 1–28. http://dx.doi.org/10.1155/2010/568375.

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Biological and artificial evolutionary systems exhibit varying degrees of evolvability and different rates of evolution. Such quantities can be affected by various factors. Here, we review some evolutionary mechanisms and discuss new developments in biology that can potentially improve evolvability or accelerate evolution in artificial systems. Biological notions are discussed to the degree they correspond to notions in Evolutionary Computation. We hope that the findings put forward here can be used to design computational models of evolution that produce significant gains in evolvability and

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37

Drăgoi, Cezara, Florin Manea, and Victor Mitrana. "Accepting Networks of Evolutionary Processors with Filtered Connections." JUCS - Journal of Universal Computer Science 13, no. (11) (2007): 1598–614. https://doi.org/10.3217/jucs-013-11-1598.

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In this paper we simplify a recent model of computation considered in [Margenstern et al. 2005], namely accepting network of evolutionary processors, by moving the filters from the nodes to the edges. Each edge is viewed as a two-way channel such that input and output filters, respectively, of the two nodes connected by the edge coincide. Thus, the possibility of controlling the computation in such networks seems to be diminished. In spite of this observation these simplified networks have the same computational power as accepting networks of evolutionary processors, that is they are computati

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38

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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39

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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40

López-ibáñez, Manuel, Juergen Branke, and Luís Paquete. "Reproducibility in Evolutionary Computation." ACM Transactions on Evolutionary Learning and Optimization 1, no. 4 (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. Fina

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

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

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

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

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

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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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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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