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Journal articles on the topic 'Neural-genetic algorithm'

Author: Grafiati

Published: 30 May 2022

Last updated: 30 July 2025

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1

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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Yan, Tai Shan. "Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network." Advanced Materials Research 532-533 (June 2012): 1785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1785.

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In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstl

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Chai, Junen. "Optimizing neural network training with Genetic Algorithms." Applied and Computational Engineering 42, no. 1 (2024): 220–24. http://dx.doi.org/10.54254/2755-2721/42/20230780.

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In modern society, computer plays an important role among all human beings. Through the increasing development of technology, some problems happened gradually. In order to solve and regenerate the country, individuals should test their strengths. This paper discusses how to use genetic algorithms to optimize neural network training. As an important tool of machine learning, neural networks have made remarkable achievements in dealing with complex tasks. However, the training process of neural networks involves a lot of hyperparameter adjustment and weight optimization, which often requires a l

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A., Gopi Kannanand, and Balasubramanian R. "An Efficient Hybrid Genetic-Grey Wolf Based Neural Network (G2NN) for Breast Cancer Data Classification." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 268–71. https://doi.org/10.35940/ijitee.A8183.1110120.

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Machine learning is the one of the famous Artificial Intelligence (AI) technique. Data Mining or Machine Learning techniques are most popular in medical diagnosis, classification, forecasting etc. K-Nearest Neighbor, SVM (Support Vector Machine), DT (Decision Tree),RF (Random Forest),NN (Neural Network) are famous classification algorithms. Neural Network is one of the popular techniques, which is used to refine the verdict of breast cancer. A neural network is otherwise known as Artificial Neural Network(ANN), which is mimicking of biological neurons of human brain. Genetic Algorithm (GA) is

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Plawiak, Pawel, and Ryszard Tadeusiewicz. "Approximation of phenol concentration using novel hybrid computational intelligence methods." International Journal of Applied Mathematics and Computer Science 24, no. 1 (2014): 165–81. http://dx.doi.org/10.2478/amcs-2014-0013.

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Abstract This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg-Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weight

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Pangesti, Witriana Endah, Indah Ariyati, Priyono Priyono, Sugiono Sugiono, and Rachmat Suryadithia. "Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks." Sinkron 9, no. 1 (2024): 276–84. http://dx.doi.org/10.33395/sinkron.v9i1.13161.

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The prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic a

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KORNING, PETER G. "TRAINING NEURAL NETWORKS BY MEANS OF GENETIC ALGORITHMS WORKING ON VERY LONG CHROMOSOMES." International Journal of Neural Systems 06, no. 03 (1995): 299–316. http://dx.doi.org/10.1142/s0129065795000226.

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In the neural network/genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley et al. (1991), he claims that, due to “the multiple representations problem”, genetic algorithms will not effectively be able to train multilayer perceptrons, whose chromosomal representation of its weights exceeds 300 bits. In the following paper, by use of a “real-life problem”, known to be non-trivial, and by a comparison with “classic” neural net training methods, I will try to show, that the modest success of applying g

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Lakshmi, V. N., and P. Nirmala. "Identification of Acral Melanoma using Genetic Algorithms Compared with Convolutional Neural Network using Dermoscopic Images." CARDIOMETRY, no. 25 (February 14, 2023): 1640–45. http://dx.doi.org/10.18137/cardiometry.2022.25.16401645.

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Aim: Identification of acral melanoma using genetic algorithm compared with convolutional neural network CNN using dermoscopic images. Materials and Methods: The study was conducted using the genetic algorithm and convolutional neural network algorithm to analyze and compare the acral melanoma detection. The number of samples used is 20, total sample size is 40. Acral melanoma is identified by evaluating the effectiveness with pre-test power of 80% (G-power), α=0.05, confidence interval 95%. Result: The proposed genetic algorithm helps in increasing the higher accuracy compared to convolutiona

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LEUNG, F. H. F., S. H. LING, and H. K. LAM. "AN IMPROVED GENETIC-ALGORITHM-BASED NEURAL-TUNED NEURAL NETWORK." International Journal of Computational Intelligence and Applications 07, no. 04 (2008): 469–92. http://dx.doi.org/10.1142/s1469026808002375.

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This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural n

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Zhang, Yong Chao, Wen Zhuang Zhao, and Jin Lian Chen. "The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.191.

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How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network i

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Chen, Siyu, Tingping Feng, Junmin Li, and Simon X. Yang. "Research on Intelligent Path Planning of Mobile Robot Based on Hybrid Symmetric Bio-Inspired Neural Network Algorithm in Complex Road Environments." Symmetry 17, no. 6 (2025): 836. https://doi.org/10.3390/sym17060836.

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To address the intelligent path planning challenges faced by mobile robots operating in complex road environments, this paper introduces the Hybrid Symmetric Bio-inspired Neural Network Algorithm (HSBNN). This algorithm integrates the improved bio-inspired neural network (BINN) with an improved genetic algorithm (IGA) and develops new models for environmental representation and path decision making, thereby significantly enhancing global optimization capabilities. The experimental results indicate that HSBNN outperforms traditional genetic algorithms, adaptive genetic algorithms, and ant colon

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MANGAL, MANISH, and MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the e

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Meng, Hua, Jie Zhu, and Ming Yu Li. "A Modeling of Vinyl Acetate Synthesis Process Based on Genetic Algorithm Optimization Neural Network." Advanced Materials Research 765-767 (September 2013): 3115–19. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.3115.

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A chemical plant in vinyl acetate synthesis reaction as the object of study, based on site data collection and mechanism analysis to determine the auxiliary variables on the basis of on-site data processing, through a combination of genetic algorithms and neural network combined to build a synthetic reaction model. The genetic algorithm is introduced to take advantage of its good global search capability to reduce the risk of limited local optimal solution. At the same time, according to the characteristics of the neural network algorithm to avoid that training is too slow, resulting in not co

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Sviridova, Svetlana, Elena Shkarupeta, and Olga Dorokhova. "The use of neural networks and a genetic algorithm for modeling the innovative environment of enterprises." E3S Web of Conferences 164 (2020): 10045. http://dx.doi.org/10.1051/e3sconf/202016410045.

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The purpose of this paper is to develop methodological tools for building the innovative environment of enterprises using the genetic algorithm and neural networks. The paper analyzes and highlights the advantages of genetic algorithms in the search for optimal solutions compared to classical methods. The scheme of construction of each step of the genetic algorithm is described in detail; the scheme of the presentation of artificial neural network data in key factors of innovative development of enterprises is given. The aspects of using neural networks of attractors and a genetic algorithm fo

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Xiao, Xue, Qing Hong Wu, and Ying Zhang. "Recognition of Paper Currency Research Based on AGA-BP Neural Network." Advanced Materials Research 989-994 (July 2014): 3968–72. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3968.

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The genetic algorithm is a randomized search method for a class of reference biological evolution of the law evolved, with global implicit parallelism inherent and better optimization. This paper presents an adaptive genetic algorithm to optimize the use of BP neural network method, namely the structure of weights and thresholds to optimize BP neural network to achieve the recognition of banknotes oriented. Experimental results show that after using genetic algorithms to optimize BP neural network controller can accurately and quickly achieved recognition effect on banknote recognition accurac

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Wu, Xiao Qin. "Research on the Optimized Algorithms on Neural Network." Advanced Materials Research 605-607 (December 2012): 2175–78. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2175.

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In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction syste

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Sibieude, Emeric, Akash Khandelwal, Pascal Girard, Jan S. Hesthaven, and Nadia Terranova. "Population pharmacokinetic model selection assisted by machine learning." Journal of Pharmacokinetics and Pharmacodynamics 49, no. 2 (2021): 257–70. http://dx.doi.org/10.1007/s10928-021-09793-6.

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AbstractA fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained usin

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Dr., Heren Chellam G. *. "IDENTIFYING OPTIMAL NUMBER OF ORTHONORMALISATION IN LEARNING ALGORITHM USING WEATHER FORECASTING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 996–1103. https://doi.org/10.5281/zenodo.60844.

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Artificial neural networks are more powerful than any other traditional expert system in the classification of patterns, which are non linear and in performing pattern classification tasks because they learn from examples without explicitly stating the rules. Multilayered feed forward neural networks possess a number of properties, which make them particularly suited to complex problems. Their applications to some real world problems are hampered by the lack of a training algorithm which finds a globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optim

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Shi, Yao-Chen, Ze-Qi Li, Tian-Xiang Zhao, Xue-Lian Yu, Chun-Mei Yin, and Yi-Shi Bai. "Fault Diagnosis of Synchronous Belt of Machine Tool Based on Improved Back Propagation Neural Network." Journal of Nanoelectronics and Optoelectronics 16, no. 12 (2021): 1972–79. http://dx.doi.org/10.1166/jno.2021.3161.

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Aiming at the problem that the machine tool synchronous belt failure during the transmission process will affect the machine tool transmission, a machine tool synchronous belt fault diagnosis method based on genetic algorithm (GA) optimized back propagation (BP) neural network is proposed. First, utilize wavelet decomposition to extract the energy characteristics of the synchronization belt fault; construct a BP neural network, and use genetic algorithms to optimize the BP neural network; finally, the energy characteristic of the vibration signal of the synchronous belt is used as the input of

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YEN, GARY, and HAIMING LU. "HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN." International Journal of Neural Systems 12, no. 01 (2002): 31–43. http://dx.doi.org/10.1142/s0129065702001023.

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In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic ti

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Yu, Sun, Huang, Wang, Wang, and Hu. "Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network." Coatings 9, no. 11 (2019): 728. http://dx.doi.org/10.3390/coatings9110728.

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This paper aimed to establish a nonlinear relationship between laser cladding process parameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to control the cracking of the cladding layer via an intelligent algorithm. By using three main process parameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was designed, and the experimental results were used as training and testing datasets for a neural network. A neural network prediction model between the laser cladding process parameters and coating crack density was establishe

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Xiao, Chang Lin, Yan Chen, Lina Liu, Ling Tong, and Ming Quan Jia. "Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks." Key Engineering Materials 500 (January 2012): 198–203. http://dx.doi.org/10.4028/www.scientific.net/kem.500.198.

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Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

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Al Khoir, M. Agus Badruzaman, and Sriyanto Sriyanto. "NEURAL NETWORK OPTIMIZATION WITH GENETIC ALGORITHM FOR HEART DISEASE PREDICTION." IJISCS (International Journal of Information System and Computer Science) 6, no. 2 (2022): 89. http://dx.doi.org/10.56327/ijiscs.v6i2.1235.

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Coronary Heart Disease (CHD) is a contributor to the number 1 cause of death in the world besides cardiovascular disease. The tendency of Indonesian people who do not know and ignore coronary heart disease is a factor that causes Indonesia to be high a contributor to deaths caused by coronary heart disease. This research is expected to produce new predictions of heart disease using genetic optimization of neural networks with better prediction results and can obtain algorithms with new percentage values in predicting coronary heart disease. Genetic optimization of the neural network is used be

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Kusnadi, Adhi, and Jansen Pratama. "Implementasi Algoritma Genetika dan Neural Network Pada Aplikasi Peramalan Produksi Mie." Jurnal ULTIMATICS 9, no. 1 (2017): 37–41. http://dx.doi.org/10.31937/ti.v9i1.562.

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Companies that produce products must be able to regulate the amount of production so that it have plan production. Therefore, it is necessary to be able to predict the amount of production. This research aims to create an application that is useful in determining the amount of production. These applications using genetic algorithms and neural network. Genetic algorithm is used to optimize the weights in the neural network. From the test results, this application uses network with 12 inputs, 5 neuron in first hidden layer, 3 neurons in the second hidden layer, and 3 neurons in the last hidden l

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Li, Bing, Anxie Tuo, Hanyue Kong, Sujiao Liu, and Jia Chen. "Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing." Computational Intelligence and Neuroscience 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/7144635.

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This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for

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Hasani Rabori, Somaieh Khajeh, and Khojaste Shour bakhloo. "Neural Networks Optimization, Using the Genetic Algorithm." IOSR Journal of Computer Engineering 18, no. 04 (2016): 102–5. http://dx.doi.org/10.9790/0661-180403102105.

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Mezghiche, Khalil M., and NourEddine Djedi. "Quantum Genetic Algorithm for Evolving Neural Controllers." Advanced Science Letters 24, no. 1 (2018): 762–66. http://dx.doi.org/10.1166/asl.2018.11810.

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Mirmirani, Sam, and H. C. Li. "Gold Price, Neural Networks and Genetic Algorithm." Computational Economics 23, no. 2 (2004): 193–200. http://dx.doi.org/10.1023/b:csem.0000021677.46295.60.

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Jafari, S. A., S. Mashohor, and M. Jalali Varnamkhasti. "Committee neural networks with fuzzy genetic algorithm." Journal of Petroleum Science and Engineering 76, no. 3-4 (2011): 217–23. http://dx.doi.org/10.1016/j.petrol.2011.01.006.

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Alsultanny, Yas Abbas, and Musbah M. Aqel. "Pattern recognition using multilayer neural-genetic algorithm." Neurocomputing 51 (April 2003): 237–47. http://dx.doi.org/10.1016/s0925-2312(02)00619-7.

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Andrade Mota, Tiago, Jorgean Ferreira Leal, and Antonio Cezar de Castro Lima. "Neural Equalizer Performance Evaluation Using Genetic Algorithm." IEEE Latin America Transactions 13, no. 10 (2015): 3439–46. http://dx.doi.org/10.1109/tla.2015.7387252.

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Yang, Dingming, Zeyu Yu, Hongqiang Yuan, and Yanrong Cui. "An improved genetic algorithm and its application in neural network adversarial attack." PLOS ONE 17, no. 5 (2022): e0267970. http://dx.doi.org/10.1371/journal.pone.0267970.

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The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms. In this paper, a novel improved genetic algorithm is proposed by improving the crossover and mutation operation of the simple genetic algorithm, and it is verified by 15 test functions. The qualitative results show that, compared with three other mainstream swarm intelligence optimization algorithms, the algorithm can not only improve the global search ability, convergence efficiency and precision, but also increase the success rate of convergen

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Pletl, Szilveszter, and Bela Lantos. "Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (2001): 81–89. http://dx.doi.org/10.20965/jaciii.2001.p0081.

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Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algo

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SEKKAL, MANSOURIA, and MOHAMMED AMINE CHIKH. "NEURO — GENETIC APPROACH TO CLASSIFICATION OF CARDIAC ARRYTHMIAS." Journal of Mechanics in Medicine and Biology 12, no. 01 (2012): 1250010. http://dx.doi.org/10.1142/s0219519412004430.

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The premature ventricular contraction (PVC) is a cardiac arrhythmia which is widely encountered in the cardiologic field. It can be detected using the electrocardiogram signal parameters. In general the use of multilayered feed forward neural networks has been hampered by the lack of a training algorithm which reliably finds a nearly globally optimal set of weights. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. This paper deals with designing a neural network clas

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Kuang, Hewu. "Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model." Journal of Function Spaces 2022 (July 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/5407319.

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten th

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Li, Jie Jia, Yong Qiang Chen, and Xiao Yan Han. "Fuzzy Neural Network Based on Genetic Algorithm for Temperature Control of Variable Air Volume Air Conditioning." Applied Mechanics and Materials 599-601 (August 2014): 952–55. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.952.

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In this paper, the theory of the fuzzy control and self-learning ability of neural network is combined, joining the genetic algorithm to optimize the fuzzy control rules, so in the light of temperature control system of variable air volume air conditioning puts forward a fuzzy neural network control method based on genetic algorithm,and this paper introduces in detail the structure, algorithm of fuzzy control and neural network. In addition,this paper verifies the superiority of the fuzzy neural network based on genetic algorithm and ordinary fuzzy neural control.

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Gamarra A., Margarita R., and Christian G. Quintero M. "Using genetic algorithm feature selection in neural classification systems for image pattern recognition." Ingeniería e Investigación 33, no. 1 (2013): 52–58. http://dx.doi.org/10.15446/ing.investig.v33n1.37667.

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Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbour classifier. The proposed approach performed better than the other methods.

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Liu, Yanchao, Limei Yan, and Jianjun Xu. "Application of Neural Network With New Hybrid Algorithm in Volcanic Rocks Seismic Prediction." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 4 (2018): 55–68. http://dx.doi.org/10.4018/ijcini.2018100103.

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This article has studied the application design and implementation of neural network with new hybrid algorithm in volcanic rocks prediction. It is considered that the convergence rate of EBP algorithm is slow, and the local minimum value can be obtained by EBP algorithm, and the approximation of global optimal value can be obtained by EBP algorithm. Therefore, genetic algorithm and EBP algorithm are proposed. The weight of the multilayer feed-forward neural network is determined by using the genetic BP algorithm. The new hybrid algorithm is applied to the neural network and volcanic oil and ga

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Rozario, Victor Stany Rozario, and Partha Sutradhar. "In-Depth Case Study on Artificial Neural Network Weights Optimization Using Meta-Heuristic and Heuristic Algorithmic Approach." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (2022): 98–109. http://dx.doi.org/10.53799/ajse.v21i2.379.

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The Meta-heuristic and Heuristic algorithms that have been introduced for deep neural network optimization is in this paper. Artificial Intelligence, and also the most used Deep Learning methods are all growing in popularity these days, thus we need faster optimization strategies for finding the results of future activities. Neural Network Optimization with Particle Swarm Optimization, Backpropagation (BP), Resilient Propagation (Rprop), and Genetic Algorithm (GA) is used for numerical analysis of different datasets and comparing each other to find out which algorithms work better for finding

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Yang, Zi. "Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses." Computational Intelligence and Neuroscience 2021 (December 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/4297600.

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Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidire

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Ding, Shifei, Chunyang Su, and Junzhao Yu. "An optimizing BP neural network algorithm based on genetic algorithm." Artificial Intelligence Review 36, no. 2 (2011): 153–62. http://dx.doi.org/10.1007/s10462-011-9208-z.

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