Academic literature on the topic 'Evolutionary Algorithms (EAs)'
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Journal articles on the topic "Evolutionary Algorithms (EAs)"
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.
Full textDao, Tran Trong. "Investigation on Evolutionary Computation Techniques of a Nonlinear System." Modelling and Simulation in Engineering 2011 (2011): 1–21. http://dx.doi.org/10.1155/2011/496732.
Full textSivadasan, J., M. Willjuice Iruthayarajan, Albert Alexander Stonier, and A. Raymon. "Design of Cross-Coupled Nonlinear PID Controller Using Single-Objective Evolutionary Algorithms." Mathematical Problems in Engineering 2023 (April 15, 2023): 1–13. http://dx.doi.org/10.1155/2023/7820047.
Full textBäck, Thomas, and Hans-Paul Schwefel. "An Overview of Evolutionary Algorithms for Parameter Optimization." Evolutionary Computation 1, no. 1 (March 1993): 1–23. http://dx.doi.org/10.1162/evco.1993.1.1.1.
Full textHeredia, Jorge Pérez. "Modelling Evolutionary Algorithms with Stochastic Differential Equations." Evolutionary Computation 26, no. 4 (December 2018): 657–86. http://dx.doi.org/10.1162/evco_a_00216.
Full textChen, Yaxin, Xin Shen, Guo Zhang, and Zezhong Lu. "Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning." Remote Sensing 15, no. 16 (August 8, 2023): 3932. http://dx.doi.org/10.3390/rs15163932.
Full textMashwani, 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.
Full textDang, Duc-Cuong, Anton Eremeev, and Per Kristian Lehre. "Escaping Local Optima with Non-Elitist Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12275–83. http://dx.doi.org/10.1609/aaai.v35i14.17457.
Full textABBASS, H. A., and R. SARKER. "THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM." International Journal on Artificial Intelligence Tools 11, no. 04 (December 2002): 531–52. http://dx.doi.org/10.1142/s0218213002001039.
Full textFonseca, Carlos M., and Peter J. Fleming. "An Overview of Evolutionary Algorithms in Multiobjective Optimization." Evolutionary Computation 3, no. 1 (March 1995): 1–16. http://dx.doi.org/10.1162/evco.1995.3.1.1.
Full textDissertations / Theses on the topic "Evolutionary Algorithms (EAs)"
Muniglia, Mathieu. "Optimisation du pilotage d'un Réacteur à Eau Pressurisée dans le cadre de la transition énergétique à l'aide d'algorithmes évolutionnaires." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS261/document.
Full textThe increase of the renewable energies contribution (as wind farms, solar energy) is a major issue in the actual context of energetic transition. The part of intermittent renewable energies is indeed forecast to be around 30% of the total production in 2030, against 6% today. On the other hand, their intermittent production may lead to an important imbalance between production and consumption. Consequently, the other ways of power production must adapt to those variations, especially nuclear energy which is the most important in France. This work aims at increasing the availability of thepower plants to load-follow, by optimizing their manageability all along their operation cycle. Among the French nuclear fleet, the pressurized water reactors(PWR) producing $1300$ electrical MW and operated in the "G" mode are considered as they show the higher capability to load-follow. In a first step, a multi-physics PWR model is designed taking as inputs the main parameters of the control rods, and computing in few minutes the criteria of interest whichare linked to the control diagram and to the effluents volume. The optimization problem which consists in minimizing those two values of interest is then solved thanks to a parallel asynchronous master-worker evolutionary algorithm. Finally, the efficient operating modes are discussed
Drouet, Valentin. "Optimisation multi-objectifs du pilotage des réacteurs nucléaires à eau sous pression en suivi de charge dans le contexte de la transition énergétique à l'aide d'algorithmes évolutionnaires." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP024.
Full textIn the context of the introduction of renewable energies in France, Nuclear Power Plant Operations are a key component for the compensation of the intermittent production of solar and wind power. In this work, we focus on the optimization of the operation cost and stability of power of a real-life power transient, while maintaining safety standards on a 1300 MW Pressurized Water Reactor, by changing the control rods management parameters. We first develop a model of the reactor targeted for load follow operations. We then propose a method based on fitness landscape analysis in order to determine the objectives that are best suited for the problem, adapt the search space and tune the optimization algorithm. We first use that method to minimize the volume of effluents and xenon oscillations at the beginning of the reactor exploitation cycle, with an asynchronous parallel algorithm based on MOEA/D for a classic day-night power transient. We then expand that study to the complete exploitation cycle, in order to find rod parameters that are efficient for the complete reactor cycle. We develop for that study an asynchronous parallel algorithm based on MOEA/D assisted by a surrogate model. The analysis of the solutions shows the need to increase the maneuvering band of the temperature regulation rods, while adjusting the overlaps of the power shimming rods
Woźniak, Ernest. "Model-based Synthesis of Distributed Real-time Automotive Architectures." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112145/document.
Full textHardware/software based solutions play significant role in the automotive domain. It is common that the implementation of certain functions that was done in a mechanical manner, in nowadays cars is done through the software and hardware. This tendency lead to the substantial number of functions operating as a set of software components deployed into hardware entities, i.e. Electronic Control Units (ECU). As a consequence the capacity of the overall code is estimated as tens of gigabytes and the number of ECUs reaches more than 100. Consequently the industrial state of the practice development approaches become inefficient. The objective of this thesis is to add to the current efforts trying to employ the Model Driven Engineering (MDE) in the context of the automotive SW/HW architectures design. First set of contributions relates to the guided strategies supporting the key engineering activities of the automotive methodology established by the EAST-\ADL2 language and the AUTOSAR standard. The main is the integration of the software architecture with the hardware platform. Although the amount of work on the synthesis is substantial, this thesis presents shortcomings of the existing approaches that disable them to fully support the EAST-ADL2/AUTOSAR methodology and delivers new techniques overcoming the current deficiencies. Second contribution concerns approaches for the modeling. Surprisingly the usage of general purpose modeling languages such as the SysML and MARTE although beneficial, haven’t found its way yet to be fully exploited by the automotive OEMs (Original Equipment Manufacturer). This especially relates to the modeling of the analyzable input and the optimization concerns which would enable triggering of the analysis and optimization directly from the models level. This work shows a way and defines additional concepts, necessary to construct analysis and optimization models
Books on the topic "Evolutionary Algorithms (EAs)"
Olivér, Gábor. CRITIQUE OF THE ASILOMAR AI PRINCIPLES = AZ ASILOMARI ELVEK KRITIKÁJA. GeniaNet Bt., 2022. http://dx.doi.org/10.15170/cotaap-2022.
Full textBook chapters on the topic "Evolutionary Algorithms (EAs)"
Drechsler, Rolf. "Applications of EAs." In Evolutionary Algorithms for VLSI CAD, 57–145. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2866-8_6.
Full textPeriaux, Jacques, Felipe Gonzalez, and Dong Seop Chris Lee. "Advanced Techniques for Evolutionary Algorithms (EAs)." In Intelligent Systems, Control and Automation: Science and Engineering, 39–52. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9520-3_4.
Full textZhou, Zhi-Hua, Yang Yu, and Chao Qian. "Boundary Problems of EAs." In Evolutionary Learning: Advances in Theories and Algorithms, 83–92. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5956-9_7.
Full textDe Jong, Kenneth. "Parameter Setting in EAs: a 30 Year Perspective." In Parameter Setting in Evolutionary Algorithms, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69432-8_1.
Full textKao, Ming-Yang, Tak-Wah Lam, Wing-Kin Sung, and Hing-Fung Ting. "A Decomposition Theorem for MaximumWeight Bipartite Matchings with Applications to Evolutionary Trees." In Algorithms - ESA’ 99, 438–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_38.
Full textJansen, Thomas, and Ingo Wegener. "On the Analysis of Evolutionary Algorithms — A Proof That Crossover Really Can Help." In Algorithms - ESA’ 99, 184–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_17.
Full textChen, Ting, and Ming-Yang Kao. "On the Informational Asymmetry between Upper and Lower Bounds for Ultrametric Evolutionary Trees." In Algorithms - ESA’ 99, 248–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48481-7_22.
Full textTaubert, Oskar, Marie Weiel, Daniel Coquelin, Anis Farshian, Charlotte Debus, Alexander Schug, Achim Streit, and Markus Götz. "Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations." In Lecture Notes in Computer Science, 106–24. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_6.
Full text"Microwave structures design using EAs." In Emerging Evolutionary Algorithms for Antennas and Wireless Communications, 161–227. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbew534e_ch5.
Full text"Antenna array design using EAs." In Emerging Evolutionary Algorithms for Antennas and Wireless Communications, 83–128. Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/sbew534e_ch3.
Full textConference papers on the topic "Evolutionary Algorithms (EAs)"
Xue, Ke, Chao Qian, Ling Xu, and Xudong Fei. "Evolutionary Gradient Descent for Non-convex Optimization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/443.
Full textQian, Chao. "Towards Theoretically Grounded Evolutionary Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/819.
Full textBian, Chao, Chao Qian, and Ke Tang. "A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/195.
Full textBonissone, Stefano R. "Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs." In International Symposium on Optical Science and Technology, edited by Bruno Bosacchi, David B. Fogel, and James C. Bezdek. SPIE, 2001. http://dx.doi.org/10.1117/12.448334.
Full textLehre, Per Kristian, and Pietro Simone Oliveto. "Runtime Analysis of Population-based Evolutionary Algorithms - Part I: Steady State EAs." In GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583133.3595056.
Full textBian, Chao, Yawen Zhou, Miqing Li, and Chao Qian. "Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/612.
Full textHao, Wang, Li Zhou, Xiaobo Zhang, and Zhanxue Wang. "Acceleration Method for Evolutionary Optimization of Variable Cycle Engine." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14369.
Full textShang, Haopu, Jia-Liang Wu, Wenjing Hong, and Chao Qian. "Neural Network Pruning by Cooperative Coevolution." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/667.
Full textAbbas, Iraq, and Qusay Al-Salami. "Inverted Generational Distance Bat Algorithm for Many-Objective Optimization Problems." In 4th International Conference on Administrative & Financial Sciences. Cihan University-Erbil, 2023. http://dx.doi.org/10.24086/icafs2023/paper.905.
Full textM, Ganga, and Gini R. "Optimized Multi Support Vector Machine Based Approach for Fake News Detection." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/jaad9174/ngcesi23p46.
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