Relevant bibliographies by topics / Neural-genetic algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural-genetic algorithm'

Author: Grafiati
Published: 30 May 2022
Last updated: 30 July 2025

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Journal articles on the topic "Neural-genetic algorithm"

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Qi, Ma. "Visual style conversion strategy for visual media based on MGADNN algorithm." Journal of Computational Methods in Sciences and Engineering 24, no. 3 (2024): 1571–84. http://dx.doi.org/10.3233/jcm-247194.

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An improved genetic algorithm is proposed to optimize the deep neural network algorithm for visual style conversion in visual media. It consists of two parts: optimizing the deep neural network algorithm design and designing a video style conversion model. The genetic algorithm selection strategy is enhanced to optimize the neural network structure. A non-recursive neural network is used to handle temporal inconsistency in a single frame. Experimental results on the Heart dataset show that the accuracy of the optimized deep neural network algorithm is 0.8913, outperforming other algorithms lik
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Al Haromainy, Muhammad Muharrom, Dwi Arman Prasetya, and Anggraini Puspita Sari. "Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data." TIERS Information Technology Journal 4, no. 1 (2023): 16–24. http://dx.doi.org/10.38043/tiers.v4i1.4326.

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Stock price data or similar time series data can be used to carry out forecasting processes using past data. The method that can be used is like a neural network, one type of neural network that is used is the Recurrent Neural Network. When using the Recurrent Neural Network (RNN) method, we need to determine the appropriate parameters in order to get the best forecasting results. It takes experience or . In this study, this problem can be solved using optimization algorithms, such as Genetic Algorithms. With genetic algorithms, neural networks can be trained to get the best objective function
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Cheng, Yu Gui. "Energy Demand Forecast of City Based on Cellular Genetic Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2122.

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As a branch of genetic algorithm (GA), cellular genetic algorithm (CGA) has been used in search optimization of the population in recent years. Compared with traditional genetic algorithm and the algorithm combined with traditional genetic algorithm and BP neural network, energy demand forecast of city by the method of combining cellular genetic algorithm and BP neural network had the characteristic of the minimum training times, the shortest consumption time and the minimum error. Meanwhile, it was better than the other two algorithms from the point of fitting effect.
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Khamis, Azme Bin, and Phang Hou Yee. "A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price." European Journal of Engineering Research and Science 3, no. 6 (2018): 10. http://dx.doi.org/10.24018/ejers.2018.3.6.758.

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The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error,
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Khamis, Azme bin, and Phang Hou Yee. "A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price." European Journal of Engineering and Technology Research 3, no. 6 (2018): 10–14. http://dx.doi.org/10.24018/ejeng.2018.3.6.758.

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The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error,
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Fakhri, Mansour, Ershad Amoosoltani, Mona Farhani, and Amin Ahmadi. "Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach." Canadian Journal of Civil Engineering 44, no. 11 (2017): 945–55. http://dx.doi.org/10.1139/cjce-2017-0124.

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The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the gen
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Tsoulos, Ioannis G., Vasileios Charilogis, and Dimitrios Tsalikakis. "Introducing a New Genetic Operator Based on Differential Evolution for the Effective Training of Neural Networks." Computers 14, no. 4 (2025): 125. https://doi.org/10.3390/computers14040125.

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Artificial neural networks are widely established models used to solve a variety of real-world problems in the fields of physics, chemistry, etc. These machine learning models contain a series of parameters that must be appropriately tuned by various optimization techniques in order to effectively address the problems that they face. Genetic algorithms have been used in many cases in the recent literature to train artificial neural networks, and various modifications have been made to enhance this procedure. In this article, the incorporation of a novel genetic operator into genetic algorithms
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Chen, Yin Ping, and Hong Xia Wu. "Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm." Advanced Materials Research 823 (October 2013): 335–39. http://dx.doi.org/10.4028/www.scientific.net/amr.823.335.

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This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorit
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Ke, Gang, and Ying Han Hong. "The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network." Applied Mechanics and Materials 599-601 (August 2014): 726–30. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.726.

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The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition an
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Wang, Hong Tao. "The Study on Neural Network Intelligent Method Based on Genetic Algorithm." Advanced Materials Research 271-273 (July 2011): 546–51. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.546.

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The paper gives the hybrid computational intelligence learning algorithm with global convergence, which is combined by BP algorithm and genetic algorithm. This algorithm connects the strengths of the BP algorithm and genetic algorithms. It not only has faster convergence, but also has a good global convergence property. The computer simulation results show that the hybrid algorithm is significantly better than the genetic algorithm and BP algorithm.
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Dissertations / Theses on the topic "Neural-genetic algorithm"

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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of variou
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Blomström, Karl. "Benchmarking an artificial neural network tuned by a genetic algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-58253.

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This thesis starts with a brief introduction to neural networks and the tuning of neural networks using genetic algorithms. An improved genetic algorithm is benchmarked using the technical paper Proben1 as a starting point. The benefits of using a genetic algorithm as well as results of the benchmark tests in comparison to a resilient backpropagation algorithm are discussed. The improved genetic algorithm is not a universal solution to all classification problems. Even though it outperforms  the resilient backpropagation algorithm slightly in these benchmark tests more benchmarking on more vas
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Murnion, Shane D. "Neural and genetic algorithm applications in GIS and remote sensing." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337024.

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McMurtrey, Shannon Dale. "Training and Optimizing Distributed Neural Networks Using a Genetic Algorithm." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/243.

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Parallelizing neural networks is an active area of research. Current approaches surround the parallelization of the widely used back-propagation (BP) algorithm, which has a large amount of communication overhead, making it less than ideal for parallelization. An algorithm that does not depend on the calculation of derivatives, and the backward propagation of errors, better lends itself to a parallel implementation. One well known training algorithm for neural networks explicitly incorporates network structure in the objective function to be minimized which yields simpler neural networks. Prior
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Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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Kopel, Ariel. "NEURAL NETWORKS PERFORMANCE AND STRUCTURE OPTIMIZATION USING GENETIC ALGORITHMS." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/840.

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Artificial Neural networks have found many applications in various fields such as function approximation, time-series prediction, and adaptive control. The performance of a neural network depends on many factors, including the network structure, the selection of activation functions, the learning rate of the training algorithm, and initial synaptic weight values, etc. Genetic algorithms are inspired by Charles Darwin’s theory of natural selection (“survival of the fittest”). They are heuristic search techniques that are based on aspects of natural evolution, such as inheritance, mutation, sele
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MacLeod, Christopher. "The synthesis of artificial neural networks using single string evolutionary techniques." Thesis, Robert Gordon University, 1999. http://hdl.handle.net/10059/367.

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The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological techniques are used to grow Artificial Neural Network topologies. The Embryological Algorithm is an alternative to the popular Genetic Algorithm, which is widely used to achieve similar results. The algorithm grows in the sense that the network structure is added to incrementally and thus changes
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Deane, Jason. "Scheduling online advertisements using information retrieval and neural network/genetic algorithm based metaheuristics." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015400.

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Fischer, Manfred M., and Yee Leung. "A Genetic Algorithm Based Evolutionary Computational Neural Network for Modelling Spatial Interaction Data." WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/4151/1/WSG_DP_6198.pdf.

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Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds
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Stivason, Charles T. "Industry Based Fundamental Analysis: Using Neural Networks and a Dual-Layered Genetic Algorithm Approach." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/40422.

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This research tests the ability of artificial learning methodologies to map market returns better than logistic regression. The learning methodologies used are neural networks and dual-layered genetic algorithms. These methodologies are used to develop a trading strategy to generate excess returns. The excess returns are compared to test the trading strategy's effectiveness. Market-adjusted and size-adjusted excess returns are calculated. Using a trading strategy based approach the logistic regression models generated greater returns than the neural network and dual-layered genetic algo
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Books on the topic "Neural-genetic algorithm"

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Vas, Peter. Artificial-intelligence-based electrical machines and drives: Application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. Oxford University Press, 1999.

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P, Dhawan Atam, Meyer Claudia M, and United States. National Aeronautics and Space Administration., eds. Genetic algorithm based input selection for a neural network function approximator with application to SSME health monitoring. National Aeronautics and Space Administration, 1991.

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Albrecht, Rudolf F., Colin R. Reeves, and Nigel C. Steele, eds. Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0.

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Pearson, David W., Nigel C. Steele, and Rudolf F. Albrecht. Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4.

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Kůrková, Věra, Roman Neruda, Miroslav Kárný, and Nigel C. Steele, eds. Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9.

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Dobnikar, Andrej, Nigel C. Steele, David W. Pearson, and Rudolf F. Albrecht. Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9.

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Book chapters on the topic "Neural-genetic algorithm"

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Alander, J. T., T. Mantere, and P. Turunen. "Genetic Algorithm Based Software Testing." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_71.

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Uesaka, Yoshinori. "Convergence of Algorithm and the Schema Theorem in Genetic Algorithms." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_56.

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Naghizadeh, Mehran. "Genetic Algorithm and Artificial Neural Network." In Dynamic of Soil in Ground-Borne Vibration Mitigation. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-44352-8_3.

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Gröbner, Matthias, and Peter Wilke. "Rostering with a Hybrid Genetic Algorithm." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_78.

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Kussul, Ernst M., and Tatyana N. Baidyk. "Genetic Algorithm for Neurocomputer Image Recognition." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_33.

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Beiu, Valeriu, and John G. Taylor. "VLSI Optimal Neural Network Learning Algorithm." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_18.

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Oppacher, F., and M. Wineberg. "A Canonical Genetic Algorithm Based Approach to Genetic Programming." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_88.

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Nagy, Ivan, Petr Nedoma, and Miroslav Kárný. "Factorized EM Algorithm for Mixture Estimation." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_100.

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Balázs, Márton-Ernő. "A Genetic Algorithm with Dynamic Population Size." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9_41.

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Alander, Jarmo T. "On Robot Navigation Using a Genetic Algorithm." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_68.

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Conference papers on the topic "Neural-genetic algorithm"

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Zhao, Zichao, Bei Chen, Haoran Ma, et al. "Efficient Hardware Configuration Method for Photonic Neural Network with Genetic Algorithm." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sm3m.2.

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The component imprecisions set obstacles for the implementation of on-chip photonic neural networks (PNNs). In this work, an efficient configuration method based on the genetic algorithm (GA) is proposed and applied for PNNs. Great convergence abilities are experimentally presented through performing complex-valued PNNs with two photonic chips.
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ROHAYANI, Hetty, Tuga MAURITSIUS, Leslie H. Spit Warnars Harco, and Edi ABDURRACHMAN. "Evaluation Performance Neural Network Genetic Algorithm." In Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/aisr.k.200424.065.

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Kajan, Slavomir, and Stefan Kozak. "Neural-genetic control algorithm of robots." In 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). IEEE, 2014. http://dx.doi.org/10.1109/raad.2014.7002243.

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Setyawati, Bina R., Robert C. Creese, and Sidharta Sahirman. "Genetic algorithm for neural networks optimization." In Optics East, edited by Bhaskaran Gopalakrishnan. SPIE, 2004. http://dx.doi.org/10.1117/12.578064.

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Tan, Xiao. "LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978299.

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Nezamoddini, Nasim, and Amirhosein Gholami. "Integrated Genetic Algorithm and Artificial Neural Network." In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2019. http://dx.doi.org/10.1109/cse/euc.2019.00057.

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Uang, Chii-Maw, Wen-Gong Chen, and Ji-Bin Horng. "Genetic-algorithm-based tri-state neural networks." In Photonics Asia 2002, edited by Guoguang Mu, Francis T. S. Yu, and Suganda Jutamulia. SPIE, 2002. http://dx.doi.org/10.1117/12.483202.

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Idrissi, Mohammed Amine Janati, Hassan Ramchoun, Youssef Ghanou, and Mohamed Ettaouil. "Genetic algorithm for neural network architecture optimization." In 2016 3rd International Conference on Logistics Operations Management (GOL). IEEE, 2016. http://dx.doi.org/10.1109/gol.2016.7731699.

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Sampath, Suresh, and Riti Singh. "An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53914.

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This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses concept of a hybrid diagnostics model in which a nested neural network is used with genetic algorithm to form a hybrid diagnostics model. The nested neural
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Chaiyaratana, Nachol, and Ali M. S. Zalzala. "Time-Optimal Path Planning and Control Using Neural Networks and a Genetic Algorithm." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24512.

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Abstract This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-dof robotic system. Extended Kohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimise command tracking errors. The results indicate that the extended Kohonen network controller is more efficient than the trajectory preshaping scheme reported in early literature. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimisation problem related to time-optimal c
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Reports on the topic "Neural-genetic algorithm"

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Rogers, Leah L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10102700.

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Guo, Boyun, Andrew Duguid, and Ronar Nygaard. Statistical Analysis of CO2 Exposed Wells to Predict Long Term Leakage through the Development of an Integrated Neural-Genetic Algorithm. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1373948.

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Sharp, David H., John Reinitz, and Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada256223.

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Sullivan, Jr, Lai John M., and Q. Application of Neural Networks Coupled with Genetic Algorithms to Optimize Soil Cleanup Operations in Cold Climates. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada637453.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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