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Journal articles on the topic 'Neural-genetic algorithm'

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Published: 30 May 2022

Last updated: 31 May 2022

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1

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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2

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 (June 8, 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, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

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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 (June 8, 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, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

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4

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 (December 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 network. Two application examples are given to illustrate the merits of the proposed network and the improved GA.

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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 algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.

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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 (November 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 genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

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7

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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8

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 and detection effect, compared with the traditional network intrusion detection algorithm.

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9

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 firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.

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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 (March 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 weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.

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11

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 (March 2011): 217–23. http://dx.doi.org/10.1016/j.petrol.2011.01.006.

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12

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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13

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

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14

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

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15

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

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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 (October 2015): 3439–46. http://dx.doi.org/10.1109/tla.2015.7387252.

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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 (February 18, 2011): 153–62. http://dx.doi.org/10.1007/s10462-011-9208-z.

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18

YEN, GARY, and HAIMING LU. "HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN." International Journal of Neural Systems 12, no. 01 (February 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 time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi-layer Perceptron networks and radial-basis function networks. Based upon the chosen cost function, a linear weight combination decision-making approach has been applied to derive an approximated Pareto-optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two-objective optimization problem.

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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 (September 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 genetic algorithms to the training of perceptrons, is caused not so much by the “multiple representations problem” as by the fact that problem-specific knowledge available is often ignored, thus making the problem unnecessarily tough for the genetic algorithm to solve. Special success is obtained by the use of a new fitness function, which takes into account the fact that the search performed by a genetic algorithm is holistic, and not local as is usually the case when perceptrons are trained by traditional methods.

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Zou, Pan, Manik Rajora, Mingyou Ma, Hungyi Chen, Wenchieh Wu, and Steven Y. Liang. "Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach." International Journal of Materials, Mechanics and Manufacturing 6, no. 2 (April 2018): 82–87. http://dx.doi.org/10.18178/ijmmm.2018.6.2.352.

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21

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 system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

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22

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 (February 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 evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.

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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 intelligent control system.

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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 conducive to practical application. For this optimized BP Neural Network Based on Self-adapted Genetic Algorithm, by comparing the simulation data obtained by the instance of the digital signal proved that this method has better prospects than traditional neural network algorithm.

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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 (May 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 convergence to the optimal value under the same experimental conditions. The quantitative results show that the algorithm performs superiorly in 13 of the 15 tested functions. The Wilcoxon rank-sum test was used for statistical evaluation, showing the significant advantage of the algorithm at 95% confidence intervals. Finally, the algorithm is applied to neural network adversarial attacks. The applied results show that the method does not need the structure and parameter information inside the neural network model, and it can obtain the adversarial samples with high confidence in a brief time just by the classification and confidence information output from the neural network.

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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 for modeling the processes of the innovative environment of enterprises are considered. The key problem of introducing effective industrial innovations is the lack of a favorable climatic environment that stimulates the creation of innovations that ensure the growth of global competitiveness, labor productivity and the quality of life of the population. The result of the study is the formation of a model of the innovative environment of enterprises based on the use of neural networks and a genetic algorithm.

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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 accuracy compared to traditional BP neural network has been greatly improved, improved network adaptive capacity and generalization ability.

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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 (October 27, 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 using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

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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 (March 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 classifier of PVC beats whose weights are genetically evolved using a genetic algorithm. We developed two classifiers. First, the Classical neural classifier (CNC) was trained by the back-propagation (BP) method. Second neuro-genetic classifier (NGC) was trained by genetic algorithm technique. Performance and accuracy of the two techniques are presented and compared. Our results illustrate the improvements gained by using a genetic algorithm rather than BP. We use the medical database (MIT-BIH) to validate our results.

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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 (March 20, 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 algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.

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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 (June 16, 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 layer. Then for the genetic algorithm parameters used were 10 individuals, 50 generations, crossover probability 0.8 and mutations probability 0.1. Based on the test results, this application has the forecasting’s accuracy rate reaches 86%. Keyword : forecasting, production forecasting, genetic algorithm neural network, optimization.

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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 (December 1, 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 the neural network, and the fault simulation test is carried out. The results show that the genetic algorithm GA-optimized BP neural network has higher accuracy than the traditional BP neural network for fault diagnosis of machine tool synchronous belt.

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Fan, Chao Hua, Yu Ting He, Hong Peng Li, and Feng Li. "Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network." Advanced Materials Research 33-37 (March 2008): 1283–88. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1283.

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Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

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Liu, Jianyong, Huaixiao Wang, Yangyang Sun, Chengqun Fu, and Jie Guo. "Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/571295.

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The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

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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 (November 3, 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 established, and a genetic algorithm was used to optimize the prediction results. To improve their prediction accuracy, genetic algorithms were used to optimize the weights and thresholds of the neural networks. In addition, the performance of the neural network was tested. The results show that the order of influence on the coating crack sensitivity was as follows: overlap rate > powder feed rate > scanning speed. The relative error between the predicted value and the experimental value of the three-group test genetic algorithm-optimized neural network model was less than 9.8%. The genetic algorithm optimized the predicted results, and the technological parameters that resulted in the smallest crack density were as follows: powder feed rate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of 0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization of the neural network and genetic algorithm process.

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Zhao, Hu Cheng. "Wavelet Neural Network Based on Chaos Genetic Algorithm." Applied Mechanics and Materials 339 (July 2013): 307–12. http://dx.doi.org/10.4028/www.scientific.net/amm.339.307.

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To improve the performance of Wavelet Neural Network (WNN), a hybrid WNN learning algorithm, which is combination of Genetic Algorithm (GA) and Chaos Optimization Algorithm (COA) in a mutual complementarity manner, is proposed. In the algorithm, GA is first used to roughly search the optimal parameters of WNN as a whole, and then COA is adopted to perform the refined search on the basis of the result obtained by GA, which can make remarkable progress in modeling accuracy, learning speed, and overcoming local convergence or precocity. Simulation show its effectiveness.

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PratapPanigrahy, Mahendra, and Neeraj Kumar. "Face Recognition using Genetic Algorithm and Neural Networks." International Journal of Computer Applications 55, no. 4 (October 20, 2012): 8–12. http://dx.doi.org/10.5120/8741-2613.

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SHIBA, Naoki, Manabu KOTANI, and Kenzo AKAZAWA. "Designing Multi-layered Neural Networks Using Genetic Algorithm." Transactions of the Society of Instrument and Control Engineers 34, no. 8 (1998): 1080–87. http://dx.doi.org/10.9746/sicetr1965.34.1080.

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Kang, Taewon. "A Compact Genetic Algorithm for Neural Networks Training." Journal of Korean Institute of Information Technology 18, no. 2 (February 28, 2020): 9–15. http://dx.doi.org/10.14801/jkiit.2020.18.2.9.

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Petridis, V., E. Paterakis, and A. Kehagias. "A hybrid neural-genetic multimodel parameter estimation algorithm." IEEE Transactions on Neural Networks 9, no. 5 (1998): 862–76. http://dx.doi.org/10.1109/72.712158.

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Nissinen, Ari S., Heikki N. Koivo, and Hannu Koivisto. "Optimization of Neural Network Topologies using Genetic Algorithm." Intelligent Automation & Soft Computing 5, no. 3 (January 1999): 211–23. http://dx.doi.org/10.1080/10798587.1999.10750762.

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Hasan, Ali, and Watheq Laith. "Online Elman Neural Network Training by Genetic Algorithm." British Journal of Mathematics & Computer Science 19, no. 1 (January 10, 2016): 1–15. http://dx.doi.org/10.9734/bjmcs/2016/29060.

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Taheri, Javid, and Albert Y. Zomaya. "A Combined Genetic-neural Algorithm for Mobility Management." Journal of Mathematical Modelling and Algorithms 6, no. 3 (March 9, 2007): 481–507. http://dx.doi.org/10.1007/s10852-007-9066-5.

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Nikbakht, Saeid, Cosmin Anitescu, and Timon Rabczuk. "Optimizing the neural network hyperparameters utilizing genetic algorithm." Journal of Zhejiang University-SCIENCE A 22, no. 6 (June 2021): 407–26. http://dx.doi.org/10.1631/jzus.a2000384.

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Roy, Prasenjit, and Baher Abdulhai. "GAID: Genetic Adaptive Incident Detection for Freeways." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 96–105. http://dx.doi.org/10.3141/1856-10.

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Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.

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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 training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

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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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Li, Xiao Fang. "Simulation on Task Scheduling for Multiprocessors Based on Improved Neural Network." Applied Mechanics and Materials 513-517 (February 2014): 2293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2293.

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This paper mainly discusses task scheduling for multiprocessors. Application requires higher performance of the multiprocessors task scheduling systems. The traditional algorithms majorly consider the accuracy and neglect the real-time performance. In order to improve the real-time performance while maintaining the accuracy, the paper proposes a task scheduling algorithm (GA-ACO) for multiprocessors based on improved neural network. It first builds mathematical models for task scheduling of multiprocessor systems, and then introduces genetic algorithms to quickly find feasible solutions. The simulation results show that the improved neural network algorithm not only has the global optimization ability of genetic algorithm, but also has both local search and the positive feedback capabilities of neural networks; compared with single optimization algorithm, it can quickly find the task scheduling solutions to meet real-time requirements, accelerate the speed of execution of the task, furthermore achieve reasonable, effective task allocation and scheduling for multi-processor.

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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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Li, Jun, Shu Lin Kan, and Peng Yu Liu. "The Study of PNN Quality Control Method Based on Genetic Algorithm." Key Engineering Materials 467-469 (February 2011): 2103–8. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2103.

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For the probability neural network (PNN) algorithm is the non-surveillance's pattern taxonomic approach, the work load major problem, moreover the category number's selection will affect the cluster performance. How to optimize PNN enabled it to play a more effective role in the classified question, this paper proposed one use genetic algorithm optimization probability neural network method: introduction the auto-adapted mechanism genetic algorithm, to the probability neural network's parameter carries on the training, formed the supervised learning probability neural network based on the genetic algorithm, overcome the probability neural network existing algorithm flaw. Then introduces this model in the quality control, guaranteed that the production process is at the control state, achieves the quality control goal. Carries on the test through the simulation experiment to this algorithm, and with the probability neural network, the BP neural network carries on the comparative analysis, proved this method accuracy is high.

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