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Статті в журналах з теми "Genetic algorithm based learning algorithm (GABL)"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 19 лютого 2023

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1

Guang, Yaqin, Shunyong Li, and Quanping Li. "Internet Financial Risk Monitoring and Evaluation Based on GABP Algorithm." Journal of Mathematics 2022 (February 9, 2022): 1–14. http://dx.doi.org/10.1155/2022/4807428.

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Анотація:

Due to the generality and particularity of Internet financial risks, it is imperative for the institutions involved to establish a sound risk prevention, control, monitoring, and management system and timely identify and alert potential risks. Firstly, the importance of Internet financial risk monitoring and evaluation is expounded. Secondly, the basic principles of backpropagation (BP) neural network, genetic algorithm (GA), and GABP algorithms are discussed. Thirdly, the weight and threshold of the BP algorithm are optimized by using the GA, and the GABP model is established. The financial risks are monitored and evaluated by the Internet financial system as the research object. Finally, GABP is further optimized by the simulated annealing (SA) algorithm. The results show that, compared with the calculation results of the BP model, the GABP algorithm can reduce the number of BP training, has high prediction accuracy, and realizes the complementary advantages of GA and BP neural network. The GABP network optimized by simulated annealing method has better global convergence, higher learning efficiency, and prediction accuracy than the traditional BP and GABP neural network, achieves better prediction effect, effectively solves the problem that the enterprise financial risk cannot be quantitatively evaluated, more accurately assesses the size of Internet financial risk, and has certain popularization value in the application of Internet financial risk prediction.

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2

Huang, Xingwang, Xuewen Zeng, Rui Han, and Xu Wang. "An enhanced hybridized artificial bee colony algorithm for optimization problems." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 87. http://dx.doi.org/10.11591/ijai.v8.i1.pp87-94.

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Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

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3

Ali B H, Baba Fakruddin, and Prakash Ramachandran. "Compressive Domain Deep CNN for Image Classification and Performance Improvement Using Genetic Algorithm-Based Sensing Mask Learning." Applied Sciences 12, no. 14 (July 7, 2022): 6881. http://dx.doi.org/10.3390/app12146881.

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The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one. Training the CNN in the compressed domain eliminates the requirement for decompression process and results in improved efficiency, minimal storage, and lesser cost. Compressive sensing (CS) is one of the effective and efficient method for signal acquisition and recovery and CNN training on CS measurements makes the entire process compact. The most popular sensing phenomenon used in CS is based on image acquisition using single pixel camera (SPC) which has complex design implementation and usually a matrix simulation is used to represent the SPC process in numerical demonstration. The CS measurements using this phenomenon are visually different from the image and to add this in the training set of the compressed learning framework, there is a need for an inverse SPC process that is to be applied all through the training and testing dataset image samples. In this paper we proposed a simple sensing phenomenon which can be implemented using the image output of a standard digital camera by retaining few pixels and forcing the rest of the pixels to zero and this reduced set of pixels is assumed as CS measurements. This process is modeled by a binary mask application on the image and the resultant image still subjectively legible for human vision and can be used directly in the training dataset. This sensing mask has very few active pixels at arbitrary locations and there is a lot of scope to heuristically learn the sensing mask suitable for the dataset. Only very few attempts had been made to learn the sensing matrix and the sole effect of this learning process on the improvement of CNN model accuracy is not reported. We proposed to have an ablation approach to study how this sensing matrix learning improves the accuracy of the basic CNN architecture. We applied CS for two class image dataset by applying a Primitive Walsh Hadamard (PWH) binary mask function and performed the classification experiment using a basic CNN. By retaining arbitrary amount of pixel in the training and testing dataset we applied CNN on the compressed measurements to perform image classification and studied and reported the model performance in terms of training and validation accuracies by varying the amount of pixels retained. A novel Genetic Algorithm-based compressive learning (GACL) method is proposed to learn the PWH mask to optimize the model training accuracy by using two different crossover techniques. In the experiment conducted for the case of compression ratio (CR) 90% by retaining only 10% of the pixels in every images both in training and testing dataset that represent two classes, the training accuracy is improved from 67% to 85% by using diagonal crossover in offspring creation of GACL. The robustness of the method is examined by applying GACL for user defined multiclass dataset and achieved better CNN model accuracies. This work will bring out the strength of sensing matrix learning which can be integrated with advanced training models to minimize the amount of information that is to be sent to central servers and will be suitable for a typical IoT frame work.

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4

Zhai, Ran, Xuebin Chen, Langtao Pei, and Zheng Ma. "A Federated Learning Framework Against Data Poisoning Attacks on the Basis of the Genetic Algorithm." Electronics 12, no. 3 (January 21, 2023): 560. http://dx.doi.org/10.3390/electronics12030560.

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Obtaining the balance between information loss and training accuracy is crucial in federated learning. Nevertheless, inadequate data quality will affect training accuracy. Here, to improve the training accuracy without affecting information loss, we propose a malicious data detection model using the genetic algorithm to resist model poisoning attack. Specifically, the model consists of three modules: (1) Participants conduct single point training on data and upload accuracy to the third-party server; (2) Formulate data scoring formula based on data quantity and quality; (3) Use the genetic algorithm to obtain the threshold which makes the score highest. Data with accuracy which exceeds this threshold can participate in cooperative training of federated learning. Before participating in training, participants’ data is optimized to oppose data poisoning attacks. Experiments on two datasets validated the effectiveness of the proposed model. It was also verified in the fashion-MNIST data set and cifar10 data set that the training accuracy of GAFL is 7.45% higher than that of the federated learning model in the fashion-MNIST data set and 8.18% in the cifar10 data set.

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5

Jiang, Xiaojun. "Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm." Complexity 2021 (April 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/5577167.

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With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.

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6

Li, Xiaojun, Chen Zhou, Qiong Tang, Jun Zhao, Fubin Zhang, Guozhen Xia, and Yi Liu. "Forecasting Ionospheric foF2 Based on Deep Learning Method." Remote Sensing 13, no. 19 (September 26, 2021): 3849. http://dx.doi.org/10.3390/rs13193849.

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In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them.

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7

Zhang, Zhi-Cheng, Xin-Min Zeng, Gen Li, Bo Lu, Ming-Zhong Xiao, and Bing-Zeng Wang. "Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China." Atmosphere 13, no. 6 (June 7, 2022): 929. http://dx.doi.org/10.3390/atmos13060929.

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Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze–Huaihe river basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB.

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8

Alaoui, Abdiya, and Zakaria Elberrichi. "Neuronal Communication Genetic Algorithm-Based Inductive Learning." Journal of Information Technology Research 13, no. 2 (April 2020): 141–54. http://dx.doi.org/10.4018/jitr.2020040109.

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The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.

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9

Xia, Qing Feng. "A Combined Algorithm Based on ELM-RBF and Genetic Algorithm." Advanced Materials Research 1049-1050 (October 2014): 1292–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1292.

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Extreme Learning Machine-Radial Basis Function (ELM-RBF) not only inherit RBF’s merit of not suffering from local minima, but also ELM’s merit of fast learning speed, Nevertheless, it is still a research hot area of how to improve the generalization ability of ELM-RBF network. Genetic Algorithms (GA) to solve optimization problem has its unique advantage. Considered on these, the paper adopted GA to optimize ELM-RBF neural network hidden layer neurons center and biases value. Experiments data results indicated that our proposed combined algorithm has better generalization performance than classical ELM-RBF, it achieved the basic anticipated task of design.

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10

Helmi, B. Hoda, Adel T. Rahmani, and Martin Pelikan. "A factor graph based genetic algorithm." International Journal of Applied Mathematics and Computer Science 24, no. 3 (September 1, 2014): 621–33. http://dx.doi.org/10.2478/amcs-2014-0045.

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Abstract We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

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11

Dang, Yanan. "Mobile Education System Based on Genetic Algorithm." Mobile Information Systems 2022 (June 29, 2022): 1–7. http://dx.doi.org/10.1155/2022/8549357.

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In order to solve the problems existing in the current mobile education system, this paper proposes a method of mobile education system based on genetic algorithm. By analyzing the research status of mobile learning and the theory and application mode of mobile learning, this method explores the combination of mobile learning theory and application mode and China’s traditional primary and secondary education mode, then puts forward a mobile teaching model suitable for primary and secondary education, and builds a mobile teaching system on this basis. The results show that, through the analysis, it is found that most of them remain at about 10%. In practice, the occupation is slightly increased only when multiple people teach, but it also belongs to an acceptable normal level, which can meet more complex functions in the future. The function of mobile learning platform needs to be expanded. For example, mobile learning forum can provide learners with a platform for communication, discussion, and sharing learning materials.

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12

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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Анотація:

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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13

HAO, Xiaohong, Yarong JIN, Yu MA, and Hengjie LI. "Fuzzy iterative learning control based on genetic algorithm." Journal of Computer Applications 33, no. 4 (October 12, 2013): 960–63. http://dx.doi.org/10.3724/sp.j.1087.2013.00960.

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14

Hirchoua, Badr, Imadeddine Mountasser, Brahim Ouhbi, and Bouchra Frikh. "Evolutionary DRL Environment: Transfer Learning-Based Genetic Algorithm." Journal of Data Intelligence 3, no. 3 (August 2022): 333–49. http://dx.doi.org/10.26421/jdi3.3-3.

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Stock markets trading has risen as a critical challenge for artificial intelligence research. The way stock markets are moving and changing pushes researchers to find more sophisticated algorithms and strategies to anticipate the market movement and changes. From the artificial intelligence perspective, such environments require artificial agents to coordinate and transfer their best experience through different generations of agents. However, the existing agents are trained using hand-crafted expert features and expert capabilities. Notwithstanding these refinements, no previous single system has come near to dominating the trading environment. We address the algorithmic trading problem utilising an evolutive learning method. Precisely, we train a multi-agent reinforcement learning algorithm that uses only self trades generated by different generations of agents. The evolution-based genetic algorithm operates as an evolutive environment that continually adapts the agent's internal strategies and tactics. Also, it pushes the system forward to generate creative behaviours for the next generations. Additionally, a deep recurrent neural network drives the mutation mechanism through the attention that dynamically encodes the memory mutation size. The winner, which is the last agent, achieved promising performances and surpassed traditional and intelligent baselines.

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15

Kurata, K., and Y. Lida. "Genetic-Algorithm-Based Machine Learning for Crop Management." IFAC Proceedings Volumes 31, no. 5 (April 1998): 109–14. http://dx.doi.org/10.1016/s1474-6670(17)42107-0.

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16

Gino Sophia, S. G., V. Ceronmani Sharmila, S. Suchitra, T. Sudalai Muthu, and B. Pavithra. "Water management using genetic algorithm-based machine learning." Soft Computing 24, no. 22 (May 14, 2020): 17153–65. http://dx.doi.org/10.1007/s00500-020-05009-0.

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17

Izadkhah, Habib. "Learning Based Genetic Algorithm for Task Graph Scheduling." Applied Computational Intelligence and Soft Computing 2019 (February 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/6543957.

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Анотація:

Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.

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18

Al Rivan, Muhammad Ezar, and Bhagaskara Bhagaskara. "Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 3 (September 14, 2020): 350. http://dx.doi.org/10.32736/sisfokom.v9i3.879.

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The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.

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19

Lin, Ying Jian, and Xiao Ji Chen. "Simulated Annealing Algorithm Improved BP Learning Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 734–37. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.734.

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BP learning algorithm has advantage of simple structure, easy to implement and so on, it has gained wide application in the malfunction diagnosis and pattern recognition etc.. For BP algorithm is easy to fall into local minima shortcoming cites simulated annealing algorithm. Firstly, study the basic idea of BP learning algorithm and its simple mathematical representation; Then, research simulated annealing algorithm theory and annealing processes; Finally, the study makes BP algorithm combine with simulated annealing algorithm to form a hybrid optimization algorithm of simulated annealing algorithm based on genetic and improved BP algorithm, and gives specific calculation steps. The results show that the content of this study give full play to their respective advantages of two algorithms, make best use of the advantages and bypass the disadvantages, whether in academic or in the application it has a very important significance.

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20

ZHAO, Xuewu, Guangliang LIU, Xindang CHENG, and Junzhong JI. "Bayesian network structure learning algorithm based on topological order and quantum genetic algorithm." Journal of Computer Applications 33, no. 6 (October 29, 2013): 1595–99. http://dx.doi.org/10.3724/sp.j.1087.2013.01595.

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21

Chang, Chun Yuan. "An Adaptive Algorithm for Forest Fire Spread Based on Genetic Algorithm." Advanced Materials Research 694-697 (May 2013): 2842–45. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2842.

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The spread of forest fire is a complex adaptive system. The spread could be seen as the result of fire agents continuous learning, adaptation and co-ordination. This paper founded an Agent-based model for forest fire spread, modeled the generating of fire spread rules based on Genetic Algorithms. Created the spread rules with effect of wind and topography independently for forest fire, designed the fitness function, and took the genetic operation on the rules, which created new rules. Implemented the adaptive algorithm on Repast S, and used it in the Agent-based model of forest fire spread. The result of models running indicated the adaptive algorithm could improve the adaptive ability of fire agent.

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22

Shi, Hai Bin, and Yi Li. "Adaptive Genetic Algorithm Based Data Classification Rules Learning System." Advanced Materials Research 271-273 (July 2011): 818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.818.

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It’s a worthy research topic to use genetic algorithm for classification rules in data mining. In this paper, it was studied and researched in-depth. Firstly, we combined genetic algorithm and machine learning together, and then analyzed architecture of the genetic algorithm-based classification system, and also its development concrete structure was given. Secondly, we proposed a data classification rules learning system based on adaptive genetic algorithm, which can learn the classification rules accurately from the dataset. Finally the standard Play Tennis dataset was used for a closed test and after learning the system got three classification rules all with 100% accuracy rate, which fully demonstrated the feasibility of this algorithm.

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23

Guo, Lin. "English Hybrid Cooperative Learning Model Based on Genetic Algorithm." Journal of Physics: Conference Series 1881, no. 2 (April 1, 2021): 022043. http://dx.doi.org/10.1088/1742-6596/1881/2/022043.

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24

T., Balamurugan, and Dr Gnanamanoharan E. "Genetic Algorithm and Deep Learning feature based Tumor Detection." Indian Journal of Computer Science and Engineering 12, no. 6 (December 20, 2021): 1837–46. http://dx.doi.org/10.21817/indjcse/2021/v12i6/211206192.

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25

Pernkopf, F., and D. Bouchaffra. "Genetic-based EM algorithm for learning Gaussian mixture models." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 8 (August 2005): 1344–48. http://dx.doi.org/10.1109/tpami.2005.162.

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26

Kubota, Naoyuki, Toshihito Morioka, Fumio Kojima, and Toshio Fukuda. "Learning of mobile robots using perception-based genetic algorithm." Measurement 29, no. 3 (April 2001): 237–48. http://dx.doi.org/10.1016/s0263-2241(00)00044-0.

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27

Wu, Baolin, and Xinghuo Yu. "Fuzzy modelling and identification with genetic algorithm based learning." Fuzzy Sets and Systems 113, no. 3 (August 2000): 351–65. http://dx.doi.org/10.1016/s0165-0114(97)00408-9.

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28

Twardowski, K. "An associative architecture for genetic algorithm-based machine learning." Computer 27, no. 11 (November 1994): 27–38. http://dx.doi.org/10.1109/2.330041.

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29

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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Анотація:

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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30

Folly, Komla A. "An Improved Population-Based Incremental Learning Algorithm." International Journal of Swarm Intelligence Research 4, no. 1 (January 2013): 35–61. http://dx.doi.org/10.4018/jsir.2013010102.

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Анотація:

Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, a PBIL with adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the diversity consistently during the run longer than the standard PBIL. The proposed algorithm is validated by applying it to power system controller parameters optimization problem. Simulation results show that the Adaptive PBIL based controller performs better than the standard PBIL based controller, in particular under small disturbance.

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31

NIKDEL, ZAHRA, and HAMID BEIGY. "A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS." International Journal of Computational Intelligence and Applications 11, no. 02 (June 2012): 1250011. http://dx.doi.org/10.1142/s1469026812500113.

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In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

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32

Miao, Liu, Zhenxing Sun, and Zhang Jie. "The Parallel Algorithm Based on Genetic Algorithm for Improving the Performance of Cognitive Radio." Wireless Communications and Mobile Computing 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/5986482.

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The intercarrier interference (ICI) problem of cognitive radio (CR) is severe. In this paper, the machine learning algorithm is used to obtain the optimal interference subcarriers of an unlicensed user (un-LU). Masking the optimal interference subcarriers can suppress the ICI of CR. Moreover, the parallel ICI suppression algorithm is designed to improve the calculation speed and meet the practical requirement of CR. Simulation results show that the data transmission rate threshold of un-LU can be set, the data transmission quality of un-LU can be ensured, the ICI of a licensed user (LU) is suppressed, and the bit error rate (BER) performance of LU is improved by implementing the parallel suppression algorithm. The ICI problem of CR is solved well by the new machine learning algorithm. The computing performance of the algorithm is improved by designing a new parallel structure and the communication performance of CR is enhanced.

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33

Jambunathan, Suriya Prakash. "A Machine Learning-Based Approach for Antenna Design Using Class_Reg Algorithm Optimized Using Genetic Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1682–86. http://dx.doi.org/10.22214/ijraset.2021.39097.

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Abstract: Microstrip patch antennas are predominantly in use in mobile communication and healthcare. Their performances are even improved, using Split-Ring Resonator cells. But finding the ideal dimensions of the microstrip patch antenna and calculating the correct number and size of the split ring resonator cells consume a lot of time when we use Electromagnetic Simulation software to design first and then simulate. Using the pre-calculated results of certain sets of microstrip patch antennas with split ring resonators, a machine learning model can be trained and hence be used to predict the antenna metrics when the dimensions are specified. When the machine learning algorithms are combined with feature-optimization algorithms such as the Genetic Algorithm, the efficiency and performance can be improved further. Keywords: Machine Learning, Micro-strip Patch Antenna, Genetic algorithm, Split Ring Resonator.

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34

Viswanathsarma, Ch, Debnath Bhattacharyya, and Hye-jin Kim. "Performance Comparison between Genetic Algorithm and Population-Based Incremental Learning Algorithm using Student Ranking Application." International Journal of Bio-Science and Bio-Technology 8, no. 6 (December 31, 2017): 129–38. http://dx.doi.org/10.14257/ijbsbt.2016.8.6.13.

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35

Ghosh, Sayani, Sayantan Dey, and Souvik Chatterjee. "Automated Breast Cancer Diagnosis Based on Machine Learning Algorithm." American Journal of Electronics & Communication 2, no. 1 (July 5, 2021): 4–9. http://dx.doi.org/10.15864/ajec.2102.

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Abstract – Breast Cancer classification is becoming more important with the increasing demand of automated applications especially interactive applications. It can be used to improve the performance of classifiers like Logistic Regression, Decision Tree, Random Forest, SVC etc. This study is based on learning genetic patterns of patients with breast tumors and machine learning algorithms that aim to demonstrate a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize different algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on accuracy, precision and the roc curves. The present report prepared by us proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms by reducing False Positive rate. In this paper, there were two challenges to automate the breast cancer diagnosis: (i) determining which model best classifies the data and (ii) how to automatically design and adjust the parameters of the machine learning model. We have summarized the experimental studies and the obtained results, and lastly presented the main conclusion.

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36

BRUDERER, E., and J. V. SINGH. "ORGANIZATIONAL EVOLUTION, LEARNING, AND SELECTION: A GENETIC-ALGORITHM-BASED MODEL." Academy of Management Journal 39, no. 5 (October 1, 1996): 1322–49. http://dx.doi.org/10.2307/257001.

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37

Bruderer, Erhard, and Jitendra V. Singh. "Organizational Evolution, Learning, and Selection: A Genetic-Algorithm-Based Model." Academy of Management Journal 39, no. 5 (October 1996): 1322–49. http://dx.doi.org/10.5465/257001.

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38

Singh, Karanpreet, and Rakesh K. Kapania. "ALGA: Active Learning-Based Genetic Algorithm for Accelerating Structural Optimization." AIAA Journal 59, no. 1 (January 2021): 330–44. http://dx.doi.org/10.2514/1.j059240.

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39

Ahn, Seyoung, Jeehyeong Kim, Soo Young Park, and Sunghyun Cho. "Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm." IEEE Access 9 (2021): 4738–51. http://dx.doi.org/10.1109/access.2020.3048348.

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40

RASHEED, KHALED, and HAYM HIRSH. "Learning to be selective in genetic-algorithm-based design optimization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 3 (June 1999): 157–69. http://dx.doi.org/10.1017/s0890060499133043.

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Анотація:

In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer.

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41

Sun, Chenchen, Derong Shen, Yue Kou, Tiezheng Nie, and Ge Yu. "A genetic algorithm based entity resolution approach with active learning." Frontiers of Computer Science 11, no. 1 (February 2017): 147–59. http://dx.doi.org/10.1007/s11704-015-5276-6.

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42

Liu, Juan, and Weihua Li. "A concept learning method based on a hybrid genetic algorithm." Science in China Series E: Technological Sciences 41, no. 5 (October 1998): 488–95. http://dx.doi.org/10.1007/bf02917023.

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43

Dwivedi, Pragya, Vibhor Kant, and Kamal K. Bharadwaj. "Learning path recommendation based on modified variable length genetic algorithm." Education and Information Technologies 23, no. 2 (August 19, 2017): 819–36. http://dx.doi.org/10.1007/s10639-017-9637-7.

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44

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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Анотація:

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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45

Chen, Ying-ping, and David E. Goldberg. "Convergence Time for the Linkage Learning Genetic Algorithm." Evolutionary Computation 13, no. 3 (September 2005): 279–302. http://dx.doi.org/10.1162/1063656054794806.

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Анотація:

This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.

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46

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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Анотація:

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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47

Duan, Xiaocong. "Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm." International Journal of Emerging Technologies in Learning (iJET) 14, no. 12 (June 27, 2019): 15. http://dx.doi.org/10.3991/ijet.v14i12.10812.

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In order to solve the automatic generation and evolution of personalized curriculum, the method of using genetic algorithm to realize the evolution of personalized learning content is proposed to solve the dynamic personalized needs of users. Through the research and implementation of personalized curriculum generation technology, firstly, the structures of curriculum generation, genetic algorithm and curriculum scene, as well as the method and technology of personalized curriculum generation and evolution system framework are described in detail. Then, the framework structure based on genetic algorithm is determined, and the user model is updated. Finally, experiments are carried out based on genetic algorithm. The research on the experiment of automatic generation and evolution of personalized curriculum shows that the application of genetic algorithm in the process of curriculum generation and evolution makes students' learning content evolve with the change of their knowledge state in the process of learning, effectively promotes students' interest in learning, and improves learning efficiency and effect.

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48

Yang, Xiao Bo, Ji Ning Feng, Zhe Jun Diao, and Hong Yun Liu. "Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 823–28. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.823.

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Анотація:

Based on studying wavelet neural network (WNN) training algorithm and geometrical structure, a new WNN optimization algorithm-hybrid hierarchy genetic is introduced. The algorithm is combined by hierarchy genetic algorithm and linear multi-regress. Hybrid hierarchy genetic algorithm (HHGA) can determine the structure and parameters of WNN from data at one time. The method has the merits of faster learning speed, higher precision. It is compared with traditional BP algorithm in this paper. The effectiveness of the algorithm is demonstrated.

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49

Zhou, Zhiyu, Hanxuan Guo, Yaming Wang, Zefei Zhu, Jiang Wu, and Xiangqi Liu. "Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm." International Journal of Advanced Robotic Systems 15, no. 4 (July 1, 2018): 172988141879299. http://dx.doi.org/10.1177/1729881418792992.

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This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

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50

Gao, Xiaoyu, Shipin Yang, and Lijuan Li. "Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2258/1/012019.

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Abstract Genetic algorithm, as a kind of evolutionary algorithm, has the characteristics of easy operation and global search, but its stochasticity is relatively strong and highly susceptible to parameters. When facing a large-scale scheduling problem, a strategy is needed to improve the parameter adaptability to make its solution more effective. Reinforcement learning, as an optimization method, has a strong autonomous learning capability. Therefore, this paper proposes a genetic algorithm based on reinforcement learning, which uses Q-learning to self-learning the crossover probability and improve the generalization ability of genetic algorithm, so as to achieve the solution of large-scale replacement flow shop scheduling problem.

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