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Journal articles on the topic 'Evolutionary computer algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Leciejewski, Sławomir, and Mariusz Szynkiewicz. "Algorithmicity of Evolutionary Algorithms." Studies in Logic, Grammar and Rhetoric 63, no. 1 (2020): 87–100. http://dx.doi.org/10.2478/slgr-2020-0029.

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Abstract In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope. The given examples will refer mostly to disciplines not directly related to computer science and to the colloquial language. In the next part we will also discuss the modifications made to the meaning of the term algorithm and how this concept is now understood in computer science. Finally, we will discuss the problem of algorithmicity of evolutionary algorithms, i.e. we w

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2

Bartz-Beielstein, Thomas, Jürgen Branke, Jörn Mehnen, and Olaf Mersmann. "Evolutionary Algorithms." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 3 (2014): 178–95. http://dx.doi.org/10.1002/widm.1124.

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3

Jiang, Dazhi, and Zhun Fan. "The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/474805.

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At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators

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4

Dioşan, Laura, and Mihai Oltean. "Evolutionary design of Evolutionary Algorithms." Genetic Programming and Evolvable Machines 10, no. 3 (2009): 263–306. http://dx.doi.org/10.1007/s10710-009-9081-6.

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5

Johannsen, Daniel, Piyush P. Kurur, and Johannes Lengler. "Evolutionary Algorithms for Quantum Computers." Algorithmica 68, no. 1 (2013): 152–89. http://dx.doi.org/10.1007/s00453-013-9784-1.

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6

Clark, David E., and David R. Westhead. "Evolutionary algorithms in computer-aided molecular design." Journal of Computer-Aided Molecular Design 10, no. 4 (1996): 337–58. http://dx.doi.org/10.1007/bf00124503.

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7

Li, Kangshun, Fahui Gu, Wei Li, and Ying Huang. "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (2018): 1959004. http://dx.doi.org/10.1142/s0218001419590043.

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Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-populatio

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8

Beyer, Hans-Georg, Hans-Paul Schwefel, and Ingo Wegener. "How to analyse evolutionary algorithms." Theoretical Computer Science 287, no. 1 (2002): 101–30. http://dx.doi.org/10.1016/s0304-3975(02)00137-8.

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9

Doerr, Benjamin, Anton Eremeev, Frank Neumann, Madeleine Theile, and Christian Thyssen. "Evolutionary algorithms and dynamic programming." Theoretical Computer Science 412, no. 43 (2011): 6020–35. http://dx.doi.org/10.1016/j.tcs.2011.07.024.

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10

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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Lameijer, Eric-Wubbo, Thomas Bäck, Joost N. Kok, and AD P. Ijzerman. "Evolutionary Algorithms in Drug Design." Natural Computing 4, no. 3 (2005): 177–243. http://dx.doi.org/10.1007/s11047-004-5237-8.

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12

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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13

García-Sánchez, P., J. González, P. A. Castillo, M. G. Arenas, and J. J. Merelo-Guervós. "Service oriented evolutionary algorithms." Soft Computing 17, no. 6 (2013): 1059–75. http://dx.doi.org/10.1007/s00500-013-0999-5.

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14

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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15

Klawonn, Frank, and Annette Keller. "Fuzzy clustering with evolutionary algorithms." International Journal of Intelligent Systems 13, no. 10-11 (1998): 975–91. http://dx.doi.org/10.1002/(sici)1098-111x(199810/11)13:10/11<975::aid-int6>3.0.co;2-w.

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16

Fernández de Vega, F., C. Cruz, L. Navarro, P. Hernández, T. Gallego, and L. Espada. "Unplugging Evolutionary Algorithms: an experiment on human-algorithmic creativity." Genetic Programming and Evolvable Machines 15, no. 4 (2014): 379–402. http://dx.doi.org/10.1007/s10710-014-9225-1.

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17

Chambel, Teresa, Luís Correia, Jônatas Manzolli, Gonçalo Dias Miguel, Nuno A. C. Henriques, and Nuno Correia. "Creating video art with evolutionary algorithms." Computers & Graphics 31, no. 6 (2007): 837–47. http://dx.doi.org/10.1016/j.cag.2007.08.004.

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18

Sutton, Andrew M. "Thomas Jansen: Analyzing Evolutionary Algorithms: The Computer Science Perspective." Genetic Programming and Evolvable Machines 14, no. 4 (2013): 473–75. http://dx.doi.org/10.1007/s10710-013-9193-x.

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19

Ibsen-Jensen, Rasmus, Krishnendu Chatterjee, and Martin A. Nowak. "Computational complexity of ecological and evolutionary spatial dynamics." Proceedings of the National Academy of Sciences 112, no. 51 (2015): 15636–41. http://dx.doi.org/10.1073/pnas.1511366112.

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There are deep, yet largely unexplored, connections between computer science and biology. Both disciplines examine how information proliferates in time and space. Central results in computer science describe the complexity of algorithms that solve certain classes of problems. An algorithm is deemed efficient if it can solve a problem in polynomial time, which means the running time of the algorithm is a polynomial function of the length of the input. There are classes of harder problems for which the fastest possible algorithm requires exponential time. Another criterion is the space requireme

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20

Shabash, Boris, and Kay C. Wiese. "Diploidy in evolutionary algorithms for dynamic optimization problems." International Journal of Intelligent Computing and Cybernetics 8, no. 4 (2015): 312–29. http://dx.doi.org/10.1108/ijicc-07-2015-0026.

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Purpose – In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue. Design/methodology/approach – In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is indi

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21

Xuesong Yan, Qinghua Wu, Chenyu Hu, and Qingzhong Liang. "Design Electronic Circuits Using Evolutionary Algorithms." Journal of Next Generation Information Technology 1, no. 1 (2010): 127–39. http://dx.doi.org/10.4156/jnit.vol1.issue1.11.

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22

Ababneh, Jehad. "Greedy particle swarm and biogeography-based optimization algorithm." International Journal of Intelligent Computing and Cybernetics 8, no. 1 (2015): 28–49. http://dx.doi.org/10.1108/ijicc-01-2014-0003.

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Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms. Findings –

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23

Reichel, Joachim, and Martin Skutella. "Evolutionary Algorithms and Matroid Optimization Problems." Algorithmica 57, no. 1 (2008): 187–206. http://dx.doi.org/10.1007/s00453-008-9253-4.

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24

Shankar, K., and Akshay S. Baviskar. "Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (2018): 20–46. http://dx.doi.org/10.1108/ijicc-12-2016-0063.

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Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search towa

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25

El-Abd, Mohammed. "Performance assessment of foraging algorithms vs. evolutionary algorithms." Information Sciences 182, no. 1 (2012): 243–63. http://dx.doi.org/10.1016/j.ins.2011.09.005.

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26

Wu, Qinghua, Bin Wu, Chengyu Hu, and Xuesong Yan. "Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm." Symmetry 13, no. 2 (2021): 322. http://dx.doi.org/10.3390/sym13020322.

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As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve

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27

Oltean, Mihai. "Evolving Evolutionary Algorithms with Patterns." Soft Computing 11, no. 6 (2006): 503–18. http://dx.doi.org/10.1007/s00500-006-0079-1.

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28

GEN, MITSUO, and RUNWEI CHENG. "EVOLUTIONARY NETWORK DESIGN: HYBRID GENETIC ALGORITHMS APPROACH." International Journal of Computational Intelligence and Applications 03, no. 04 (2003): 357–80. http://dx.doi.org/10.1142/s1469026803001075.

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In recent years we have evidenced an extensive effort in the development of computer communication networks, which have deeply integrated in human being's everyday life. One of the important aspects of the network design process is the topological design problem involved in establishing a communication network. However, with the increase of the problem scale, the conventional techniques are facing the challenge to effectively and efficiently solve those complicated network design problems. In this article, we give a brief survey on our recent research works of network design problems by using

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29

Gabor, Thomas, Thomy Phan, and Claudia Linnhoff-Popien. "Productive fitness in diversity-aware evolutionary algorithms." Natural Computing 20, no. 3 (2021): 363–76. http://dx.doi.org/10.1007/s11047-021-09853-3.

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AbstractIn evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion o

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30

LENGLER, J., and A. STEGER. "Drift Analysis and Evolutionary Algorithms Revisited." Combinatorics, Probability and Computing 27, no. 4 (2018): 643–66. http://dx.doi.org/10.1017/s0963548318000275.

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One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-cont

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31

Eiben, A. E., R. Hinterding, and Z. Michalewicz. "Parameter control in evolutionary algorithms." IEEE Transactions on Evolutionary Computation 3, no. 2 (1999): 124–41. http://dx.doi.org/10.1109/4235.771166.

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32

LaTorre, Antonio, José-María Peña, Santiago Muelas, and Alex A. Freitas. "Learning hybridization strategies in evolutionary algorithms." Intelligent Data Analysis 14, no. 3 (2010): 333–54. http://dx.doi.org/10.3233/ida-2010-0424.

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33

Alden, Matthew E., Daniel M. Bryan, Brenton J. Lessley, and Arindam Tripathy. "Detection of Financial Statement Fraud Using Evolutionary Algorithms." Journal of Emerging Technologies in Accounting 9, no. 1 (2012): 71–94. http://dx.doi.org/10.2308/jeta-50390.

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ABSTRACT In this paper, we use a Genetic Algorithm (GA) and MARLEDA—a modern Estimation of Distribution Algorithm (EDA)—to evolve and train several fuzzy rule-based classifiers (FRBCs) to detect patterns of financial statement fraud. We find that both GA and MARLEDA demonstrate a better ability to classify unseen corporate data observations than those of a traditional logistic regression model, and provide validity for detecting financial statement fraud with Evolutionary Algorithms (EAs) and FRBCs. Using ten-fold cross-validation, the GA and MARLEDA yield average training classification accur

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34

Preux, P., and E. G. Talbi. "Towards hybrid evolutionary algorithms." International Transactions in Operational Research 6, no. 6 (1999): 557–70. http://dx.doi.org/10.1111/j.1475-3995.1999.tb00173.x.

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35

Mashwani, Wali Khan, Ruqayya Haider, and Samir Brahim Belhaouari. "A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems." Complexity 2021 (February 27, 2021): 1–18. http://dx.doi.org/10.1155/2021/5521951.

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Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have succes

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36

Fernandes, Carlos M., Nuno Fachada, Juan L. J. Laredo, J. J. Merelo, and Agostinho C. Rosa. "Population sizing of cellular evolutionary algorithms." Swarm and Evolutionary Computation 58 (November 2020): 100721. http://dx.doi.org/10.1016/j.swevo.2020.100721.

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37

REICHELT, DIRK, and FRANZ ROTHLAUF. "RELIABLE COMMUNICATION NETWORK DESIGN WITH EVOLUTIONARY ALGORITHMS." International Journal of Computational Intelligence and Applications 05, no. 02 (2005): 251–66. http://dx.doi.org/10.1142/s146902680500160x.

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For the reliable communication network design (RCND) problem unreliable links are available, each bearing several options which have different levels of reliability and varying costs. The goal is to find the most cost-effective communication network design that satisfies a predefined overall reliability constraint. This paper presents two new evolutionary algorithm (EA) approaches to solving the RCND problem: LaBORNet and BaBORNet. LaBORNet uses an encoding that represents the network topology as well as the used link options while repairing infeasible solutions using an additional repair heur

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38

Łapa, Krystian, Krzysztof Cpałka, Łukasz Laskowski, Andrzej Cader, and Zhigang Zeng. "Evolutionary Algorithm with a Configurable Search Mechanism." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (2020): 151–71. http://dx.doi.org/10.2478/jaiscr-2020-0011.

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AbstractIn this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, differ

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39

Sahni, Srishti, Vaibhav Aggarwal, Ashish Khanna, Deepak Gupta, and Siddhartha Bhattacharyya. "Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution." Journal of information and organizational sciences 44, no. 2 (2020): 345–63. http://dx.doi.org/10.31341/jios.44.2.9.

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Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson's Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Wei

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40

Goldberg, David E. "Genetic and evolutionary algorithms come of age." Communications of the ACM 37, no. 3 (1994): 113–19. http://dx.doi.org/10.1145/175247.175259.

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Xiao, Renbin, Zhenwu Tao, and Yong Liu. "Isomorphism Identification of Kinematic Chains Using Novel Evolutionary Approaches." Journal of Computing and Information Science in Engineering 5, no. 1 (2005): 18–24. http://dx.doi.org/10.1115/1.1846057.

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This paper presents a new method to isomorphism identification based on two novel evolutionary approaches—ant algorithm (AA) and artificial immune system (AIS). Salient features of the two evolutionary approaches are their efficient, robust and general-purpose algorithms for isomorphism identification despite its nondeterministic polynomial (NP) hard nature. First, based on the rearrangement of the vertexes in kinematic chains, the isomorphism identification of kinematic chains is transformed into a degree-reducible traveling salesman problem (TSP), so that the dimension and complexity can be

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42

Junzhou, Huo, Chen Jing, Zhou Jianjun, and Wu Hanyang. "Multi-objective Human-computer Co-operative Co-evolutionary Method Based on Non-dominated Sorting Strategy." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 213–17. http://dx.doi.org/10.2174/1874129001408010213.

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Based on the human-computer cooperation ideas, a Human-Computer Multi-Objective Cooperative Co-Evolutionary Method (HCMCCM) is developed to solve the complex engineering layout problem, in which the multiobjective optimization idea is integrated to avoid the "flooding" phenomenon that occurs during the combination of the artificial solutions and the algorithm solutions. In the proposed HCMCCM, the artificial solutions expressed by unified encoding strings are incorporated together with the algorithms solutions to create new cooperative solutions based on the non-dominated sorting strategies. T

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43

Pierreval, H., and J. L. Paris. "Distributed evolutionary algorithms for simulation optimization." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 1 (2000): 15–24. http://dx.doi.org/10.1109/3468.823477.

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AL-SHARHAN, SALAH, and FAWAZ AL-ANZI. "A HYBRID EVOLUTIONARY ALGORITHM FOR MULTIPLE-DESTINATIONS ROUTING PROBLEM." International Journal of Computational Intelligence and Applications 04, no. 04 (2004): 337–53. http://dx.doi.org/10.1142/s1469026804001355.

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This paper presents a hybrid evolutionary algorithm for constrained multiple destinations routing problem. The problem can be formulated as minimising tree cost under several constraints or QoS metrics. Computing such constrained multicast tree has been proven to be NP-complete. The proposed hybrid algorithm is based on a population based incremental learning algorithm and a constrained distance network heuristic (or CKMB) algorithm. In the proposed algorithm, CKMB is utilised as a decoding scheme. Experimental results show that, in most cases, the proposed algorithm yields better solutions th

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45

Esquivel, Susana C., Héctor Ariel Leiva, and Raúl H. Gallardt. "Selection Mechanisms in Evolutionary Algorithms." Fundamenta Informaticae 35, no. 1-4 (1998): 17–33. http://dx.doi.org/10.3233/fi-1998-35123402.

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46

Lâm, Bùi Thu. "MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS: FOUNDATION, DEVELOPMENT AND OPEN ISSUES." Journal of Computer Science and Cybernetics 33, no. 3 (2018): 193–212. http://dx.doi.org/10.15625/1813-9663/33/3/11111.

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Evolutionary computation (EC) has been a fascinating branch of computation inspiredby a natural phenomenal of evolution. EC enables computer scientists to design eective algorithmsdealing dicult problems. This paper focuses on a special class problem called multi-objective optimizationproblems and evolutionary algorithms designed for it. We will overview the development ofmulti-objective evolutionary algorithms (MOEAs) over the years and problem diculties and thenindicate the open problems in this area. Our chief goal is to provide readers reference material in thearea of multi-objective evolu

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M. Sami Soliman, and Guanzheng Tan. "Conditional Sensor Deployment Using Evolutionary Algorithms." Journal of Convergence Information Technology 5, no. 2 (2010): 146–54. http://dx.doi.org/10.4156/jcit.vol5.issue2.17.

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48

Nesmachnow, Sergio, Héctor Cancela, and Enrique Alba. "Heterogeneous computing scheduling with evolutionary algorithms." Soft Computing 15, no. 4 (2010): 685–701. http://dx.doi.org/10.1007/s00500-010-0594-y.

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Vala, Tejas M., Vipul N. Rajput, Zong Woo Geem, Kartik S. Pandya, and Santosh C. Vora. "Revisiting the performance of evolutionary algorithms." Expert Systems with Applications 175 (August 2021): 114819. http://dx.doi.org/10.1016/j.eswa.2021.114819.

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Smith, Stephen L., Patrick Gaughan, David M. Halliday, Quan Ju, Nabil M. Aly, and Jeremy R. Playfer. "Diagnosis of Parkinson’s disease using evolutionary algorithms." Genetic Programming and Evolvable Machines 8, no. 4 (2007): 433–47. http://dx.doi.org/10.1007/s10710-007-9043-9.

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