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Relevant bibliographies by topics / Evolutionary Algorithms

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Academic literature on the topic 'Evolutionary Algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Evolutionary Algorithms"

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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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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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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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Dissertations / Theses on the topic "Evolutionary Algorithms"

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Reimann, Axel. "Evolutionary algorithms and optimization." Doctoral thesis, [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=969093497.

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Ciftci, Erhan. "Evolutionary Algorithms In Design." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12607983/index.pdf.

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Evolutionary Structural Optimization (ESO) is a relatively new design tool used to improve and optimise the design of structures. In this method, a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. In structural design, there is the demand for the development of design tools and methods that includes optimization. This need is the reason behind the development of methods like Evolutionary Structural Optimization (ESO). It is also

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Loshchilov, Ilya. "Surrogate-Assisted Evolutionary Algorithms." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00823882.

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Les Algorithmes Évolutionnaires (AEs) ont été très étudiés en raison de leur capacité à résoudre des problèmes d'optimisation complexes en utilisant des opérateurs de variation adaptés à des problèmes spécifiques. Une recherche dirigée par une population de solutions offre une bonne robustesse par rapport à un bruit modéré et la multi-modalité de la fonction optimisée, contrairement à d'autres méthodes d'optimisation classiques telles que les méthodes de quasi-Newton. La principale limitation de AEs, le grand nombre d'évaluations de la fonction objectif, pénalise toutefois l'usage des AEs pour

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Maitre, Ogier. "GPGPU for Evolutionary Algorithms." Strasbourg, 2011. http://www.theses.fr/2011STRA6240.

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Les algorithmes évolutionnaires permettent de trouver des réponses satisfaisantes, mais non-nécessairement optimales à des problèmes complexes. La puissance de ces algorithmes est directement corrélée à la puissance de calcul disponible pour leur exécution. En effet, ces algorithmes réalisent une exploration en parallèle de l’espace de recherche, par le biais de l’évolution d’une population d’individus plus ou moins adaptés à la résolution du problème. La puissance de calcul disponible contraint la taille de la population et donc la capacité d’exploration ou d’exploitation qu’offre un algorith

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Rohlfshagen, Philipp. "Molecular Algorithms for Evolutionary Computation." Thesis, University of Birmingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522032.

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Smith, James Edward. "Self adaptation in evolutionary algorithms." Thesis, University of the West of England, Bristol, 1998. http://eprints.uwe.ac.uk/11046/.

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Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive a

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Williams, Kenneth Peter. "Evolutionary algorithms for automatic parallelization." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265665.

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Shen, Liang. "Evolutionary algorithms with mixed strategy." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/f08f9fe9-f4d1-48cd-aa17-3218eb2f4f35.

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During the last several decades, many kinds of population based Evolutionary Algorithms have been developed and considerable work has been devoted to computational methods which are inspired by biological evolution and natural selection, such as Evolutionary Programming and Clonal Selection Algorithm. The objective of these algorithms is not only to find suitable adjustments to the current population and hence the solution, but also to perform the process efficiently. However, a parameter setting that was optimal at the beginning of the algorithm may become unsuitable during the evolutionary p

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Srikanth, Veturi. "Evolutionary algorithms for currency trading." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619749.

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Karunarathne, Lalith. "Network coding via evolutionary algorithms." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/57047/.

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Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complex

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Books on the topic "Evolutionary Algorithms"

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Davis, Lawrence David, Kenneth De Jong, Michael D. Vose, and L. Darrell Whitley, eds. Evolutionary Algorithms. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1542-4.

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Pétrowski, Alain, and Sana Ben-Hamida. Evolutionary Algorithms. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119136378.

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Spears, William M. Evolutionary Algorithms. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04199-4.

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Kita, Eisuke. Evolutionary algorithms. InTech, 2011.

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Abraham, Ajith, Crina Grosan, and Hisao Ishibuchi, eds. Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6.

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Jansen, Thomas. Analyzing Evolutionary Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-17339-4.

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Crina, Grosan, Abraham Ajith 1968-, and Ishibuchi Hisao, eds. Hybrid evolutionary algorithms. Springer, 2007.

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Jörg, Biethahn, and Nissen Volker 1965-, eds. Evolutionary algorithms in management applications. Springer, 1995.

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Yu, Xinjie, and Mitsuo Gen. Introduction to Evolutionary Algorithms. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-129-5.

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Yu, Xinjie. Introduction to evolutionary algorithms. Springer, 2010.

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Book chapters on the topic "Evolutionary Algorithms"

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Druckmann, Shaul. "Evolutionary Algorithms." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_159.

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Michalewicz, Zbigniew, Robert Hinterding, and Maciej Michalewicz. "Evolutionary Algorithms." In Fuzzy Evolutionary Computation. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_1.

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Pappa, Gisele L., and Alex A. Freitas. "Evolutionary Algorithms." In Natural Computing Series. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02541-9_3.

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Drechsler, Rolf. "Evolutionary Algorithms." In Evolutionary Algorithms for VLSI CAD. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2866-8_2.

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Schoenauer, Marc. "Evolutionary Algorithms." In Handbook of Evolutionary Thinking in the Sciences. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9014-7_28.

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Mattfeld, Dirk C. "Evolutionary Algorithms." In Evolutionary Search and the Job Shop. Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-662-11712-5_4.

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Grosan, Crina, and Ajith Abraham. "Evolutionary Algorithms." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21004-4_14.

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Langford, John, Xinhua Zhang, Gavin Brown, et al. "Evolutionary Algorithms." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_270.

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Eiben, Ágoston E., and James E. Smith. "Evolutionary Algorithms." In Handbook of Memetic Algorithms. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23247-3_2.

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Petrowski, Alain, and Sana Ben Hamida. "Evolutionary Algorithms." In Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45403-0_6.

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Conference papers on the topic "Evolutionary Algorithms"

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Diosan, Laura Silvia, and Mihai Oltean. "Evolving evolutionary algorithms using evolutionary algorithms." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274008.

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Merelo, Juan J. "Fluid evolutionary algorithms." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586476.

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Lourenço, Nuno, Francisco Pereira, and Ernesto Costa. "Evolving evolutionary algorithms." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330794.

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Doerr, Benjamin, Mahmoud Fouz, and Carsten Witt. "Quasirandom evolutionary algorithms." In the 12th annual conference. ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830749.

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Hitomi, Nozomi, and Daniel Selva. "A Hyperheuristic Approach to Leveraging Domain Knowledge in Multi-Objective Evolutionary Algorithms." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59870.

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Evolutionary algorithms have shown much success in solving real-world design problems, but they are considered computationally inefficient because they rely on many objective-function evaluations instead of leveraging domain knowledge to guide the optimization. An evolutionary algorithm’s performance can be improved by utilizing operators called domain-specific heuristics that incorporate domain knowledge, but existing knowledge-intensive algorithms utilize one or two domain-specific heuristics, which limits the amount of incorporated knowledge or treats all knowledge as equally effective. We

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AbuKwaik, Mohammad, Yousef Sardahi, and Ahmad M. Alshorman. "Optimizing Control Systems: A Comparative Analysis of 77 Multi-Objective Evolutionary Algorithms." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-140634.

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Abstract Multi-objective optimization is integral to control systems, particularly in engineering, where multiple objectives must be concurrently satisfied. This study pioneers a comprehensive comparison of 77 evolutionary algorithms for optimizing a second-order system under a proportional-integral-derivative (PID) controller. We emphasize the challenges of balancing conflicting metrics like peak time and percentage overshoot and how PID controller tuning is crucial in achieving this balance. To assess the algorithms’ performance, we utilize four different performance metrics, which encompass

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Scott, Eric O., and Kenneth A. De Jong. "Understanding Simple Asynchronous Evolutionary Algorithms." In FOGA '15: Foundations of Genetic Algorithms XIII. ACM, 2015. http://dx.doi.org/10.1145/2725494.2725509.

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Corns, Steven M., Kenneth M. Bryden, and Daniel A. Ashlock. "Evolutionary Optimization Using Graph Based Evolutionary Algorithms." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41287.

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Graph based evolutionary algorithms (GBEAs) are a novel evolutionary optimization technique that utilize population graphing to impose a topology or geography on the evolving solution set. In many cases in nature, the ability of a particular member of a population to mate and reproduce is limited. The factors creating these limits vary widely and include geographical distance, mating rituals, and others. The effect of these factors is to limit the mating pool, reducing the rate of spread of genetic characteristics, and increased diversity within the population. GBEAs mimic these factors result

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Goldberg, Leslie Ann. "Evolutionary Dynamics on Graphs." In FOGA '15: Foundations of Genetic Algorithms XIII. ACM, 2015. http://dx.doi.org/10.1145/2725494.2725495.

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Zukai Tang. "Comparison between hierarchical distributed evolutionary algorithms and general distributed evolutionary algorithms." In 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). IEEE, 2013. http://dx.doi.org/10.1109/mec.2013.6885405.

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Reports on the topic "Evolutionary Algorithms"

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Bosl, W. Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/917509.

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Olson, Colin C., Jonathan M. Nichols, Carl A. Villarruel, and Frank Bucholtz. On the Potential Use of Evolutionary Algorithms for Electro-Optic System Design. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada544028.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th

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Homaifar, Abdollah, Albert Esterline, and Bahram Kimiaghalam. Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada455904.

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ZHANG, Dongmei, Hui JIN, and Wei LIU. Spatial Trend Surface Analysis and Geochemical Anomaly Evaluation Based on Two Stage GEP Evolutionary Algorithm. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0116.

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