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Journal articles on the topic 'Evolutionary Algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Skitsko, Volodymyr I., and Mykola Yu Voinikov. "Supply Chain Management Using Evolutionary Algorithms." PROBLEMS OF ECONOMY 3, no. 61 (2024): 240–48. http://dx.doi.org/10.32983/2222-0712-2024-3-240-248.

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The era of digital transformation has made it possible to accumulate large amounts of data that can be used in the decision-making process, in particular in supply chain management. With the complication of the problems to be solved, classical optimization methods lose their effectiveness and do not allow to obtain a solution in an acceptable time, which creates the need to study another suitable tools, among which there are evolutionary algorithms that use the principles of biological evolution, allowing to obtain solutions close to optimal (or even exactly optimal) in an acceptable time. Evo

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2

Khera, Vansh. "Comparative Study of Evolutionary Algorithms." International Journal of Science and Research (IJSR) 12, no. 6 (2023): 836–40. http://dx.doi.org/10.21275/sr23610122607.

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Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5

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Bäck, Thomas. "Evolutionary algorithms." ACM SIGBIO Newsletter 12, no. 2 (1992): 26–31. http://dx.doi.org/10.1145/130686.130691.

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5

Graña, Manuel. "Evolutionary algorithms." Information Sciences 133, no. 3-4 (2001): 101–2. http://dx.doi.org/10.1016/s0020-0255(01)00079-2.

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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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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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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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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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Afathi, Maan. "Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (2021): 6–14. http://dx.doi.org/10.15587/1729-4061.2021.245222.

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function

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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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Luan, Yuxuan, Junjiang He, Jingmin Yang, Xiaolong Lan, and Geying Yang. "Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning." International Journal of Intelligent Systems 2023 (November 10, 2023): 1–21. http://dx.doi.org/10.1155/2023/1666735.

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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and

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Maan, Afathi. "Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (2021): 6–14. https://doi.org/10.15587/1729-4061.2021.245222.

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The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function

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Tran Binh Minh, Nguyen Long, and Thai Trung Kien. "An adaptive reference point technique to improve the quality of decomposition based multi-objective evolutionary algorithm." Journal of Military Science and Technology, CSCE7 (December 30, 2023): 3–14. http://dx.doi.org/10.54939/1859-1043.j.mst.csce7.2023.3-14.

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Applying multi-objective evolutionary optimization algorithms in solving multi-objective optimization problems is a research field that has received attention recently. In the literature of this research field, many studies have been carried out to propose multi-objective evolutionary algorithms or improve published algorithms. However, balancing the exploitation and exploration capabilities of the algorithm during the evolution process is still challenging. This article proposes an approach to solve that equilibrium problem based on analyzing population distribution during the evolutionary pr

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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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Strasser, Shane, John Sheppard, Nathan Fortier, and Rollie Goodman. "Factored Evolutionary Algorithms." IEEE Transactions on Evolutionary Computation 21, no. 2 (2017): 281–93. http://dx.doi.org/10.1109/tevc.2016.2601922.

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Berlich, R., and M. Kunze. "Parallel evolutionary algorithms." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (2003): 467–70. http://dx.doi.org/10.1016/s0168-9002(03)00471-6.

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Whitney, W., S. Rana, J. Dzubera, and K. E. Mathias. "Evaluating evolutionary algorithms." Artificial Intelligence 84, no. 1-2 (1996): 357–58. http://dx.doi.org/10.1016/0004-3702(96)81371-3.

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Whitley, Darrell, Soraya Rana, John Dzubera, and Keith E. Mathias. "Evaluating evolutionary algorithms." Artificial Intelligence 85, no. 1-2 (1996): 245–76. http://dx.doi.org/10.1016/0004-3702(95)00124-7.

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Cui, Feng-Zhe, Zhi-Zheng Xu, Xiu-Kun Wang, Chong-Quan Zhong, and Hong-Fei Teng. "A dual-system cooperative co-evolutionary algorithm for satellite equipment layout optimization." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 13 (2017): 2432–57. http://dx.doi.org/10.1177/0954410017715280.

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This paper develops a new dual-system cooperative co-evolutionary algorithm for multi-modules (or multi-bearing plate) satellite equipment layout optimization problem, based upon the Potter’s cooperative co-evolutionary framework. Firstly, a new dual-system framework based on the Potter’s cooperative co-evolutionary is constructed and then, corresponding system decomposition rule, matrix analysis method and coordination mechanism are presented. Finally, the way of matching algorithms (e.g. evolutionary algorithms and swarm intelligence algorithms) with systems A and B in the dual system is pre

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Chatain, Peter, Rocky Garg, and Lauren Tompkins. "Evolutionary Algorithms for Tracking Algorithm Parameter Optimization." EPJ Web of Conferences 251 (2021): 03071. http://dx.doi.org/10.1051/epjconf/202125103071.

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The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software

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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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Hashem, M. M. A., Keigo Watanabe, and Kiyotaka Izumi. "Stable-Optimum Gain Tuning for Designing Mobile Robot Controllers Using Incest Prevented Evolution." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 5 (1998): 164–75. http://dx.doi.org/10.20965/jaciii.1998.p0164.

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We present an evolution strategy (ES) algorithm - incest prevented evolution strategy (IPES) enhancing our novel evolution strategy (NES) algorithm. Validity of NES and IPES algorithms is compared with other evolutionary algorithms (EAs) and relative performances and also compared with test function results. The IPES algorithm shows the highest balance between exploration and exploitation over the NES algorithm on these test functions by achieving high-precision global results. Both algorithms are applied to solve stabilizing optimum gain tuning problems in mobile robot controllers. Two optima

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Gottlieb, Jens, Elena Marchiori, and Claudio Rossi. "Evolutionary Algorithms for the Satisfiability Problem." Evolutionary Computation 10, no. 1 (2002): 35–50. http://dx.doi.org/10.1162/106365602317301763.

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Several evolutionary algorithms have been proposed for the satisfiability problem. We review the solution representations suggested in literature and choose the most promising one the bit string representation for further evaluation. An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorithm for the satisfi-ability problem. The key features of successful evolutionary algorithms are identified, thereby providing useful methodological guidelines for designing new heuristics. Our results indicat

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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Di

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Zhao, Yan Wei, J. L. Zhang, and D. J. Peng. "Open Vehicle Routing Problem Using Quantum Evolutionary Algorithm." Advanced Materials Research 102-104 (March 2010): 807–12. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.807.

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Open vehicle routing problem is a kind of special vehicle routing problem, in which the vehicles do not return the depots after completing the task. Aiming at open vehicle routing problem, the mathematical model was founded by introducing virtual depots. A quantum evolutionary algorithm combined with local optimization algorithms was proposed in this paper, in which 0-1 matrix encoding was used to construct chromosomes, rotation gate with adaptively adjusting rotation angle was used to realize evolution, nearest neighbors and 2-Opt were incorporated to further improve solutions. Based on bench

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Zhang, Rui, Zhiteng Wang, and Hongjun Zhang. "Quantum-Inspired Evolutionary Algorithm for Continuous Space Optimization Based on Multiple Chains Encoding Method of Quantum Bits." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/620325.

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This study proposes a novel quantum evolutionary algorithm called four-chain quantum-inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum-inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency

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Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furth

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Chernov, Ivan E., and Andrey V. Kurov. "APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic

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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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Barros, Rodrigo C., Márcio P. Basgalupp, André C. P. L. F. de Carvalho, and Alex A. Freitas. "Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms." Evolutionary Computation 21, no. 4 (2013): 659–84. http://dx.doi.org/10.1162/evco_a_00101.

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This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT

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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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Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using sever

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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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Xu, Hong, Zijing Niu, Bo Jiang, et al. "ERRT-GA: Expert Genetic Algorithm with Rapidly Exploring Random Tree Initialization for Multi-UAV Path Planning." Drones 8, no. 8 (2024): 367. http://dx.doi.org/10.3390/drones8080367.

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In unmanned aerial vehicle (UAV) path planning, evolutionary algorithms are commonly used due to their ability to handle high-dimensional spaces and wide generality. However, traditional evolutionary algorithms have difficulty with population initialization and may fall into local optima. This paper proposes an improved genetic algorithm (GA) based on expert strategies, including a novel rapidly exploring random tree (RRT) initialization algorithm and a cross-variation process based on expert guidance and the wolf pack search algorithm. Experimental results on baseline functions in different s

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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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Zhao, Fuqing, Wenchang Lei, Weimin Ma, Yang Liu, and Chuck Zhang. "An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems." Mathematical Problems in Engineering 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/8010346.

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A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhi

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Sikora, Tomasz, and Wanda Gryglewicz-Kacerka. "APPLICATION OF GENETIC ALGORITHMS TO THE TRAVELING SALESMAN PROBLEM." Applied Computer Science 19, no. 2 (2023): 55–62. http://dx.doi.org/10.35784/acs-2023-14.

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The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows setting selected parameters of the evolutionary algorithm and solving the prepared problem. The re

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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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Lagos, Carolina, Broderick Crawford, Enrique Cabrera, Ricardo Soto, José-Miguel Rubio, and Fernando Paredes. "Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/745921.

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Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one consi

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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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Schwehr, Peter. "Evolutionary Algorithms In Architecture." Open House International 36, no. 1 (2011): 16–24. http://dx.doi.org/10.1108/ohi-01-2011-b0003.

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Change is a reliable constant. Constant change calls for strategies in managing everyday life and a high level of flexibility. Architecture must also rise to this challenge. The architect Richard Buckminster Fuller claimed that “A room should not be fixed, should not create a static mood, but should lend itself to change so that its occupants may play upon it as they would upon a piano (Krausse 2001).” This liberal interpretation in architecture defines the ability of a building to react to (ever-) changing requirements. The aim of the project is to investigate the flexibility of buildings usi

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Zhou, Junyi, Haowen Zheng, Shaole Li, et al. "A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection." Applied Sciences 14, no. 11 (2024): 4501. http://dx.doi.org/10.3390/app14114501.

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In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. T

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Misiak, Marcin. "Evolutionary Algorithms in Astrodynamics." International Journal of Astronomy and Astrophysics 06, no. 04 (2016): 435–39. http://dx.doi.org/10.4236/ijaa.2016.64035.

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WANG, Yong, Zi-Xing CAI, Yu-Ren ZHOU, and Chi-Xin XIAO. "Constrained Optimization Evolutionary Algorithms." Journal of Software 20, no. 1 (2009): 11–29. http://dx.doi.org/10.3724/sp.j.1001.2009.00011.

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Yu, Xinjie, and Mitsuo Gen. "Introduction to Evolutionary Algorithms." Industrial Engineering and Management Systems 9, no. 4 (2010): 348–49. http://dx.doi.org/10.7232/iems.2010.9.4.348.

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Alba, E., and M. Tomassini. "Parallelism and evolutionary algorithms." IEEE Transactions on Evolutionary Computation 6, no. 5 (2002): 443–62. http://dx.doi.org/10.1109/tevc.2002.800880.

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Bryden, K. M., D. A. Ashlock, S. Corns, and S. J. Willson. "Graph-based evolutionary algorithms." IEEE Transactions on Evolutionary Computation 10, no. 5 (2006): 550–67. http://dx.doi.org/10.1109/tevc.2005.863128.

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Kim, Jong-Han, and Min-Jea Tahk. "Accelerated Co-evolutionary Algorithms." International Journal of Aeronautical and Space Sciences 3, no. 1 (2002): 50–60. http://dx.doi.org/10.5139/ijass.2002.3.1.050.

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Li, Bingdong, Jinlong Li, Ke Tang, and Xin Yao. "Many-Objective Evolutionary Algorithms." ACM Computing Surveys 48, no. 1 (2015): 1–35. http://dx.doi.org/10.1145/2792984.

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