Academic literature on the topic 'Genetic algorithm based learning algorithm (GABL)'
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Journal articles on the topic "Genetic algorithm based learning algorithm (GABL)"
Guang, Yaqin, Shunyong Li, and Quanping Li. "Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm." Journal of Mathematics 2022 (February 9, 2022): 1–14. http://dx.doi.org/10.1155/2022/4807428.
Full textHuang, Xingwang, Xuewen Zeng, Rui Han, and Xu Wang. "An enhanced hybridized artificial bee colony algorithm for optimization problems." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 87. http://dx.doi.org/10.11591/ijai.v8.i1.pp87-94.
Full textAli B H, Baba Fakruddin, and Prakash Ramachandran. "Compressive Domain Deep CNN for Image Classification and Performance Improvement Using Genetic Algorithm-Based Sensing Mask Learning." Applied Sciences 12, no. 14 (July 7, 2022): 6881. http://dx.doi.org/10.3390/app12146881.
Full textZhai, Ran, Xuebin Chen, Langtao Pei, and Zheng Ma. "A Federated Learning Framework Against Data Poisoning Attacks on the Basis of the Genetic Algorithm." Electronics 12, no. 3 (January 21, 2023): 560. http://dx.doi.org/10.3390/electronics12030560.
Full textJiang, Xiaojun. "Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm." Complexity 2021 (April 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/5577167.
Full textLi, Xiaojun, Chen Zhou, Qiong Tang, Jun Zhao, Fubin Zhang, Guozhen Xia, and Yi Liu. "Forecasting Ionospheric foF2 Based on Deep Learning Method." Remote Sensing 13, no. 19 (September 26, 2021): 3849. http://dx.doi.org/10.3390/rs13193849.
Full textZhang, Zhi-Cheng, Xin-Min Zeng, Gen Li, Bo Lu, Ming-Zhong Xiao, and Bing-Zeng Wang. "Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China." Atmosphere 13, no. 6 (June 7, 2022): 929. http://dx.doi.org/10.3390/atmos13060929.
Full textAlaoui, Abdiya, and Zakaria Elberrichi. "Neuronal Communication Genetic Algorithm-Based Inductive Learning." Journal of Information Technology Research 13, no. 2 (April 2020): 141–54. http://dx.doi.org/10.4018/jitr.2020040109.
Full textXia, Qing Feng. "A Combined Algorithm Based on ELM-RBF and Genetic Algorithm." Advanced Materials Research 1049-1050 (October 2014): 1292–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1292.
Full textHelmi, B. Hoda, Adel T. Rahmani, and Martin Pelikan. "A factor graph based genetic algorithm." International Journal of Applied Mathematics and Computer Science 24, no. 3 (September 1, 2014): 621–33. http://dx.doi.org/10.2478/amcs-2014-0045.
Full textDissertations / Theses on the topic "Genetic algorithm based learning algorithm (GABL)"
El-Nainay, Mustafa Y. "Island Genetic Algorithm-based Cognitive Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28297.
Full textPh. D.
Tamaddoni, Nezhad Alireza. "Logic-based machine learning using a bounded hypothesis space : the lattice structure, refinement operators and a genetic algorithm approach." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/29849.
Full textSuleiman, Iyad. "Integrating data mining and social network techniques into the development of a Web-based adaptive play-based assessment tool for school readiness." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7293.
Full textLe, Bin. "Building a Cognitive Radio: From Architecture Definition to Prototype Implementation." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28320.
Full textPh. D.
Almejalli, Khaled A. "Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4264.
Full textDam, Hai Huong Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.
Full textCastro, Neto Henrique de. "Uma nova abordagem de aprendizagem de máquina combinando elicitação automática de casos, aprendizagem por reforço e mineração de padrões sequenciais para agentes jogadores de damas." Universidade Federal de Uberlândia, 2016. https://repositorio.ufu.br/handle/123456789/18143.
Full textAgentes que operam em ambientes onde as tomadas de decisão precisam levar em conta, além do ambiente, a atuação minimizadora de um oponente (tal como nos jogos), é fundamental que o agente seja dotado da habilidade de, progressivamente, traçar um perĄl de seu adversário que o auxilie em seu processo de seleção de ações apropriadas. Entretanto, seria improdutivo construir um agente com um sistema de tomada de decisão baseado apenas na elaboração desse perĄl, pois isso impediria o agente de ter uma Şidentidade própriaŤ, o que o deixaria a mercê de seu adversário. Nesta direção, este trabalho propõe um sistema automático jogador de Damas híbrido, chamado ACE-RL-Checkers, dotado de um mecanismo dinâmico de tomada de decisões que se adapta ao perĄl de seu oponente no decorrer de um jogo. Em tal sistema, o processo de seleção de ações (movimentos) é conduzido por uma composição de Rede Neural de Perceptron Multicamadas e biblioteca de casos. No caso, a Rede Neural representa a ŞidentidadeŤ do agente, ou seja, é um módulo tomador de decisões estático já treinado e que faz uso da técnica de Aprendizagem por Reforço TD( ). Por outro lado, a biblioteca de casos representa o módulo tomador de decisões dinâmico do agente que é gerada pela técnica de Elicitação Automática de Casos (um tipo particular de Raciocínio Baseado em Casos). Essa técnica possui um comportamento exploratório pseudo-aleatório que faz com que a tomada de decisão dinâmica do agente seja guiada, ora pelo perĄl de jogo do adversário, ora aleatoriamente. Contudo, ao conceber tal arquitetura, é necessário evitar o seguinte problema: devido às características inerentes à técnica de Elicitação Automática de Casos, nas fases iniciais do jogo Ű em que a quantidade de casos disponíveis na biblioteca é extremamente baixa em função do exíguo conhecimento do perĄl do adversário Ű a frequência de tomadas de decisão aleatórias seria muito elevada, o que comprometeria o desempenho do agente. Para atacar tal problema, este trabalho também propõe incorporar à arquitetura do ACE-RLCheckers um terceiro módulo, composto por uma base de regras de experiência extraída a partir de jogos de especialistas humanos, utilizando uma técnica de Mineração de Padrões Sequenciais. O objetivo de utilizar tal base é reĄnar e acelerar a adaptação do agente ao perĄl de seu adversário nas fases iniciais dos confrontos entre eles. Resultados experimentais conduzidos em torneio envolvendo ACE-RL-Checkers e outros agentes correlacionados com este trabalho, conĄrmam a superioridade da arquitetura dinâmica aqui proposta.
ake into account, in addition to the environment, the minimizing action of an opponent (such as in games), it is fundamental that the agent has the ability to progressively trace a proĄle of its adversary that aids it in the process of selecting appropriate actions. However, it would be unsuitable to construct an agent with a decision-making system based on only the elaboration of this proĄle, as this would prevent the agent from having its Şown identityŤ, which would leave it at the mercy of its opponent. Following this direction, this work proposes an automatic hybrid Checkers player, called ACE-RL-Checkers, equipped with a dynamic decision-making mechanism, which adapts to the proĄle of its opponent over the course of the game. In such a system, the action selection process (moves) is conducted through a composition of Multi-Layer Perceptron Neural Network and case library. In the case, Neural Network represents the ŞidentityŤ of the agent, i.e., it is an already trained static decision-making module and makes use of the Reinforcement Learning TD( ) techniques. On the other hand, the case library represents the dynamic decision-making module of the agent, which is generated by the Automatic Case Elicitation technique (a particular type of Case-Based Reasoning). This technique has a pseudo-random exploratory behavior, which makes the dynamic decision-making on the part of the agent to be directed, either by the game proĄle of the opponent or randomly. However, when devising such an architecture, it is necessary to avoid the following problem: due to the inherent characteristics of the Automatic Case Elicitation technique, in the game initial phases, in which the quantity of available cases in the library is extremely low due to low knowledge content concerning the proĄle of the adversary, the decisionmaking frequency for random decisions is extremely high, which would be detrimental to the performance of the agent. In order to attack this problem, this work also proposes to incorporate onto the ACE-RL-Checkers architecture a third module composed of a base of experience rules, extracted from games played by human experts, using a Sequential Pattern Mining technique. The objective behind using such a base is to reĄne and accelerate the adaptation of the agent to the proĄle of its opponent in the initial phases of their confrontations. Experimental results conducted in tournaments involving ACE-RL-Checkers and other agents correlated with this work, conĄrm the superiority of the dynamic architecture proposed herein.
Tese (Doutorado)
(20390), Baolin Wu. "Fuzzy modelling and identification with genetic algorithms based learning." Thesis, 1996. https://figshare.com/articles/thesis/Fuzzy_modelling_and_identification_with_genetic_algorithms_based_learning/21345057.
Full textModelling is an essential step towards a solution to complex system problems. Traditional mathematical methods are inadequate in describing the complex systems when the complexity increases. Fuzzy logic has provided an alternative way in dealing with complexity in real world.
This thesis looks at a practical approach for complex system modelling using fuzzy logic. This approach is usually called fuzzy modelling. The main aim of this thesis is to explore the capabilities of fuzzy logic in complex system modelling using available data. The fuzzy model concerned is the Sugeno-Takage-Kang model (TSK model). A genetic algorithm based learning algorithm (GABL) is proposed for fuzzy modelling. It basically contains four blocks, namely the partition, GA, tuning and termination blocks. The functioning of each block is described and the proposed algorithm is tested using a number of examples from different applications such as function approximation and processing control.
Weng, Kuei-Sung, and 翁桂松. "Fuzzy Modeling Based on Genetic Ellipsoid Learning Algorithm." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/74814313454610298473.
Full text國立臺北科技大學
機電整合研究所
90
The theme of this thesis is to apply Genetic Algorithm (GA) and Gustafson-Kessel (G-K) Algorithm to the fuzzy modeling. A method called Genetic Ellipsoid Learning Algorithm (GELA) is proposed to learn the decision regions for pattern recognition and adaptive fuzzy modeling in this thesis. 1.First topics, a learning method based on fuzzy clustering and adaptively tuned hyperellipsoids is proposed to learn the decision regions for pattern recognition. The Gustafson- Kessel (G-K) algorithm for fuzzy clustering is modified in such a way that the Genetic Algorithm is applied to dynamically learn the volumes of hyperellipsoids in G-K algorithm. The decision regions are accurately learned by the proposed method in this paper so that on one hand, misclassification errors are minimized; on the other hand, the range of learned decision regions are not too wide to reduce the accuracy of pattern recognition. 2.Second topics, a method called Genetic Ellipsoid Learning Algorithm (GELA) is proposed for adaptive fuzzy modeling integrating Genetic Algorithm (GA) and Gustafson-Kessel (G-K) Algorithm. Since G-K algorithm is able to efficiently cover data points with multiple ellipsoids, GA is applied to estimate volume of each ellipsoid. Based on the volume learned by GA as well as input/output data points, G-K algorithm will then estimate the parameters of each ellipsoid. As input/output data points are clustered by multiple ellipsoids, a second GA is proposed to fine-tune the parameters of each ellipsoid for fuzzy modeling.
Tzou, Tsung-Fei, and 鄒璁飛. "The Reinforcement Learning Behavior Unit Weights Searching based on Genetic Algorithm." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/67380234386530334498.
Full text國立中正大學
電機工程所
95
This thesis proposes a scheme based on Stochastic Searching Network and (GA) Genetic Algorithm, and we use Reinforcement Learning method for action network weights searching problem. The SGRL learning scheme is a hybrid Genetic Algorithm, which integrates the Stochastic Searching Network and the Genetic Algorithm to fulfill the Reinforcement Learning action network weights searching task. Structurally, the SGRL learning system is composed of two integrated feed-forward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the SGRL learning system, where the action network is a normal neural network. Using the TD (Temporal Difference) prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA and according to the plant dynamic reference model to adapt itself according to the internal reinforcement signal. The key concept of the SGRL learning scheme is to formulate the internal reinforcement signal contributed by the reference plant model as the fitness function for the GA. Computer simulations on controlling of the Acrobot (i.e. possessing fewer actuators than degrees of freedom) system and mountain-car system have been conducted to illustrate the performance and applicability of the proposed learning controller scheme.
Book chapters on the topic "Genetic algorithm based learning algorithm (GABL)"
Menéndez, Héctor, and David Camacho. "A Genetic Graph-Based Clustering Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2012, 216–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_27.
Full textZhu, Kenny Q., and Ziwei Liu. "Population Diversity in Permutation-Based Genetic Algorithm." In Machine Learning: ECML 2004, 537–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_49.
Full textTao, Jili, Ridong Zhang, and Yong Zhu. "Further Idea on Optimal Q-Learning Fuzzy Energy Controller for FC/SC HEV." In DNA Computing Based Genetic Algorithm, 261–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2_10.
Full textChandra Shekar, K., Priti Chandra, and K. Venugopala Rao. "Relative-Feature Learning through Genetic-Based Algorithm." In Proceedings of the Second International Conference on Computational Intelligence and Informatics, 69–79. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8228-3_8.
Full textBarka, Kamel, Lyamine Guezouli, Samir Gourdache, and Sara Ameghchouche. "Mobility Based Genetic Algorithm for Heterogeneous Wireless Networks." In Machine Learning for Networking, 93–106. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70866-5_6.
Full textEl-Shorbagy, M. A., A. Y. Ayoub, I. M. El-Desoky, and A. A. Mousa. "A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 92–101. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_10.
Full textLiu, Kai, and Jin Tian. "Subspace Learning with an Archive-Based Genetic Algorithm." In Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018, 181–88. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3402-3_20.
Full textAlexander, Vimala, and Pethalakshmi Annamalai. "An Elitist Genetic Algorithm Based Extreme Learning Machine." In Advances in Intelligent Systems and Computing, 301–9. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0251-9_29.
Full textZhu, Chen, and Jing Liu. "A Direction based Multi-Objective Agent Genetic Algorithm." In Intelligent Data Engineering and Automated Learning – IDEAL 2013, 210–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41278-3_26.
Full textLi, Bin, and Zhen-quan Zhuang. "Genetic Algorithm Based-On the Quantum Probability Representation." In Intelligent Data Engineering and Automated Learning — IDEAL 2002, 500–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_75.
Full textConference papers on the topic "Genetic algorithm based learning algorithm (GABL)"
Yichen, Liu, Li Bo, Zhao Chenqian, and Ma Teng. "Intelligent Frequency Assignment Algorithm Based on Hybrid Genetic Algorithm." In 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00-50.
Full textDong, Li-yan, Guang-yuan Liu, Sen-miao Yuan, Yong-li Li, and Zhen Li. "Classifier Learning Algorithm Based on Genetic Algorithms." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.214.
Full textLi, Mingwei, Na Qin, Tao Zhu, Yongjie Mao, and Jiaxi Zhao. "Carrier Aircraft Scheduling Optimization Based on A* Algorithm and Genetic Algorithm." In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022. http://dx.doi.org/10.1109/ddcls55054.2022.9858589.
Full textFa-Chao Li, Lian-Qing Su, and Hai-Chao Ran. "The fuzzy genetic algorithm based on rule." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527356.
Full textJiu-Ling Zhao, Jiu-Fen Zhao, and Jian-Jun Li. "Intrusion detection based on clustering genetic algorithm." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527621.
Full textZhang, Lifeng, Qiuxuan Wu, Xiaoni Chi, Jian Wang, Botao Zhang, Weijie Lin, Sergey A. Chepinskiy, Anton A. Zhilenkov, Yanbin Luo, and Farong Gao. "RNA genetic algorithm based on octopus learning mechanism." In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2021. http://dx.doi.org/10.1109/rcar52367.2021.9517596.
Full textLiu, Hai, Bin Jiao, Long Peng, and Ting Zhang. "Extreme learning machine based on improved genetic algorithm." In 5th International Conference on Information Engineering for Mechanics and Materials. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icimm-15.2015.38.
Full textSemenikhin, S. V., and L. A. Denisova. "Learning to rank based on modified genetic algorithm." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819080.
Full textHirchoua, Badr, Imadeddine Mountasser, Brahim Ouhbi, and Bouchra Frikh. "Evolutionary Deep Reinforcement Learning Environment: Transfer Learning-Based Genetic Algorithm." In iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487664.3487698.
Full textJing-Kai Li, Jian Chen, and Hua-Qing Min. "A classification method based on Immune Genetic Algorithm." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359531.
Full textReports on the topic "Genetic algorithm based learning algorithm (GABL)"
TEACHING-LEARNING BASED OPTIMIZATION METHOD CONSIDERING BUCKLING AND SLENDERNESS RESTRICTION FOR SPACE TRUSSES. The Hong Kong Institute of Steel Construction, March 2022. http://dx.doi.org/10.18057/ijasc.2022.18.1.3.
Full textMultiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.
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