Relevant bibliographies by topics / Genetic algorithm based learning algorithm (GABL)

Academic literature on the topic 'Genetic algorithm based learning algorithm (GABL)'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Genetic algorithm based learning algorithm (GABL)"

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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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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Genetic algorithm based learning algorithm (GABL)"

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El-Nainay, Mustafa Y. "Island Genetic Algorithm-based Cognitive Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28297.

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The heterogeneity and complexity of modern communication networks demands coupling network nodes with intelligence to perceive and adapt to different network conditions autonomously. Cognitive Networking is an emerging networking research area that aims to achieve this goal by applying distributed reasoning and learning across the protocol stack and throughout the network. Various cognitive node and cognitive network architectures with different levels of maturity have been proposed in the literature. All of them adopt the idea of coupling network devices with sensors to sense network conditions, artificial intelligence algorithms to solve problems, and a reconfigurable platform to apply solutions. However, little further research has investigated suitable reasoning and learning algorithms. In this dissertation, we take cognitive network research a step further by investigating the reasoning component of cognitive networks. In a deviation from previous suggestions, we suggest the use of a single flexible distributed reasoning algorithm for cognitive networks. We first propose an architecture for a cognitive node in a cognitive network that is general enough to apply to future networking challenges. We then introduce and justify our choice of the island genetic algorithm (iGA) as the distributed reasoning algorithm. Having introduced our cognitive node architecture, we then focus on the applicability of the island genetic algorithm as a single reasoning algorithm for cognitive networks. Our approach is to apply the island genetic algorithm to different single and cross layer communication and networking problems and to evaluate its performance through simulation. A proof of concept cognitive network is implemented to understand the implementation challenges and assess the island genetic algorithm performance in a real network environment. We apply the island genetic algorithm to three problems: channel allocation, joint power and channel allocation, and flow routing. The channel allocation problem is a major challenge for dynamic spectrum access which, in turn, has been the focal application for cognitive radios and cognitive networks. The other problems are examples of hard cross layer problems. We first apply the standard island genetic algorithm to a channel allocation problem formulated for the dynamic spectrum cognitive network environment. We also describe the details for implementing a cognitive network prototype using the universal software radio peripheral integrated with our extended implementation of the GNU radio software package and our island genetic algorithm implementation for the dynamic spectrum channel allocation problem. We then develop a localized variation of the island genetic algorithm, denoted LiGA, that allows the standard island genetic algorithm to scale and apply it to the joint power and channel allocation problem. In this context, we also investigate the importance of power control for cognitive networks and study the effect of non-cooperative behavior on the performance of the LiGA. The localized variation of the island genetic algorithm, LiGA, is powerful in solving node-centric problems and problems that requires only limited knowledge about network status. However, not every communication and networking problems can be solved efficiently in localized fashion. Thus, we propose a generalized version of the LiGA, namely the K-hop island genetic algorithm, as our final distributed reasoning algorithm proposal for cognitive networks. The K-hop island genetic algorithm is a promising algorithm to solve a large class of communication and networking problems with controllable cooperation and migration scope that allows for a tradeoff between performance and cost. We apply it to a flow routing problem that includes both power control and channel allocation. For all problems simulation results are provided to quantify the performance of the island genetic algorithm variation. In most cases, simulation and experimental results reveal promising performance for the island genetic algorithm. We conclude our work with a discussion of the shortcomings of island genetic algorithms without guidance from a learning mechanism and propose the incorporation of two learning processes into the cognitive node architecture to solve slow convergence and manual configuration problems. We suggest the cultural algorithm framework and reinforcement learning techniques as candidate leaning techniques for implementing the learning processes. However, further investigation and implementation is left as future work.
Ph. D.
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Tamaddoni, Nezhad Alireza. "Logic-based machine learning using a bounded hypothesis space : the lattice structure, refinement operators and a genetic algorithm approach." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/29849.

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Rich representation inherited from computational logic makes logic-based machine learning a competent method for application domains involving relational background knowledge and structured data. There is however a trade-off between the expressive power of the representation and the computational costs. Inductive Logic Programming (ILP) systems employ different kind of biases and heuristics to cope with the complexity of the search, which otherwise is intractable. Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol and Aleph). However, the structure of the search space and the properties of the refinement operators for theses systems have not been previously characterised. The contributions of this thesis can be summarised as follows: (i) characterising the properties, structure and morphisms of bounded subsumption lattice (ii) analysis of bounded refinement operators and stochastic refinement and (iii) implementation and empirical evaluation of stochastic search algorithms and in particular a Genetic Algorithm (GA) approach for bounded subsumption. In this thesis we introduce the concept of bounded subsumption and study the lattice and cover structure of bounded subsumption. We show the morphisms between the lattice of bounded subsumption, an atomic lattice and the lattice of partitions. We also show that ideal refinement operators exist for bounded subsumption and that, by contrast with general subsumption, efficient least and minimal generalisation operators can be designed for bounded subsumption. In this thesis we also show how refinement operators can be adapted for a stochastic search and give an analysis of refinement operators within the framework of stochastic refinement search. We also discuss genetic search for learning first-order clauses and describe a framework for genetic and stochastic refinement search for bounded subsumption. on. Finally, ILP algorithms and implementations which are based on this framework are described and evaluated.
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Suleiman, Iyad. "Integrating data mining and social network techniques into the development of a Web-based adaptive play-based assessment tool for school readiness." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7293.

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A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly.
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Le, Bin. "Building a Cognitive Radio: From Architecture Definition to Prototype Implementation." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28320.

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Cognitive radio (CR) technology introduces a revolutionary wireless communication mechanism in terminals and network segments, so that they are able to learn their environment and adapt intelligently to the most appropriate way of providing the service for the user's exact need. By supporting multi-band, mode-mode cognitive applications, the cognitive radio addresses an interactive way of managing the spectrum that harmonizes technology, market and regulation. This dissertation gives a complete story of building a cognitive radio. It goes through concept clarification, architecture definition, functional block building, system integration, and finally to the implementation of a fully-functional cognitive radio node prototype that can be directly packaged for application use. This dissertation starts with a comprehensive review of CR research from its origin to today. Several fundamental research issues are then addressed to let the reader know what makes CR a challenging and interesting research area. Then the CR system solution is introduced with the details of its hierarchical functional architecture called the Egg Model, modular software system called the cognitive engine, and the kernel machine learning mechanism called the cognition cycle. Next, this dissertation discusses the design of specific functional building blocks which incorporate environment awareness, solution making, and adaptation. These building blocks are designed to focus on the radio domain that mainly concerns the radio environment and the radio platform. Awareness of the radio environment is achieved by extracting the key environmental features and applying statistical pattern recognition methods including artificial neural networks and k-nearest neighbor clustering. Solutions for the radio behavior are made according to the recognized environment and the previous knowledge through case based reasoning, and further adapted or optimized through genetic algorithm solution search. New experiences are gained through the practice of the new solution, and thus the CR's knowledge evolves for future use; therefore, the CR's performance continues improving with this reinforcement learning approach. To deploy the solved solution in terms of the radio's parameters, a platform independent radio interface is designed. With this general radio interface, the algorithms in the cognitive engine software system can be applied to various radio hardware platforms. To support and verify designed cognitive algorithms and cognitive functionalities, a complete reconfigurable SDR platform, called the CWT2 waveform framework, is designed in this dissertation. In this waveform framework, a hierarchical configuration and control system is constructed to support flexible, real-time waveform reconfigurability. Integrating all the building blocks described above allows a complete CR node system. Based on this general CR node structure, a fully-functional Public Safety Cognitive Radio (PSCR) node is prototyped to provide the universal interoperability for public safety communications. Although the complete PSCR node software system has been packaged to an official release including installation guide and user/developer manuals, the process of building a cognitive radio from concept to a functional prototype is not the end of the CR research; on-going and future research issues are addressed in the last chapter of the dissertation.
Ph. D.
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Almejalli, Khaled A. "Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4264.

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The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.
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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the problem distributed in nature. Distributed stream data mining poses many challenges to the data mining community including scalability and coping with changes in the underlying concept over time. In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently in distributed stream data mining. LCSs are an incremental learner, and being evolutionary based they are inherently adaptive. However, they suffer from two main drawbacks that hinder their use as fast data mining algorithms. First, they require a large population size, which slows down the processing of arriving instances. Second, they require a large number of parameter settings, some of them are very sensitive to the nature of the learning problem. As a result, it becomes difficult to choose a right setup for totally unknown problems. The aim of this thesis is to attack these two problems in LCS, with a specific focus on UCS - a supervised evolutionary learning classifier system. UCS is chosen as it has been tested extensively on classification tasks and it is the supervised version of XCS, a state of the art LCS. In this thesis, the architectural design for a distributed stream data mining system will be first introduced. The problems that UCS should face in a distributed data stream task are confirmed through a large number of experiments with UCS and the proposed architectural design. To overcome the problem of large population sizes, the idea of using a Neural Network to represent the action in UCS is proposed. This new system - called NLCS { was validated experimentally using a small fixed population size and has shown a large reduction in the population size needed to learn the underlying concept in the data. An adaptive version of NLCS called ANCS is then introduced. The adaptive version dynamically controls the population size of NLCS. A comprehensive analysis of the behaviour of ANCS revealed interesting patterns in the behaviour of the parameters, which motivated an ensemble version of the algorithm with 9 nodes, each using a different parameter setting. In total they cover all patterns of behaviour noticed in the system. A voting gate is used for the ensemble. The resultant ensemble does not require any parameter setting, and showed better performance on all datasets tested. The thesis concludes with testing the ANCS system in the architectural design for distributed environments proposed earlier. The contributions of the thesis are: (1) reducing the UCS population size by an order of magnitude using a neural representation; (2) introducing a mechanism for adapting the population size; (3) proposing an ensemble method that does not require parameter setting; and primarily (4) showing that the proposed LCS can work efficiently for distributed stream data mining tasks.
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Castro, Neto Henrique de. "Uma nova abordagem de aprendizagem de máquina combinando elicitação automática de casos, aprendizagem por reforço e mineração de padrões sequenciais para agentes jogadores de damas." Universidade Federal de Uberlândia, 2016. https://repositorio.ufu.br/handle/123456789/18143.

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Fundação de Amparo a Pesquisa do Estado de Minas Gerais
Agentes que operam em ambientes onde as tomadas de decisão precisam levar em conta, além do ambiente, a atuação minimizadora de um oponente (tal como nos jogos), é fundamental que o agente seja dotado da habilidade de, progressivamente, traçar um perĄl de seu adversário que o auxilie em seu processo de seleção de ações apropriadas. Entretanto, seria improdutivo construir um agente com um sistema de tomada de decisão baseado apenas na elaboração desse perĄl, pois isso impediria o agente de ter uma Şidentidade própriaŤ, o que o deixaria a mercê de seu adversário. Nesta direção, este trabalho propõe um sistema automático jogador de Damas híbrido, chamado ACE-RL-Checkers, dotado de um mecanismo dinâmico de tomada de decisões que se adapta ao perĄl de seu oponente no decorrer de um jogo. Em tal sistema, o processo de seleção de ações (movimentos) é conduzido por uma composição de Rede Neural de Perceptron Multicamadas e biblioteca de casos. No caso, a Rede Neural representa a ŞidentidadeŤ do agente, ou seja, é um módulo tomador de decisões estático já treinado e que faz uso da técnica de Aprendizagem por Reforço TD( ). Por outro lado, a biblioteca de casos representa o módulo tomador de decisões dinâmico do agente que é gerada pela técnica de Elicitação Automática de Casos (um tipo particular de Raciocínio Baseado em Casos). Essa técnica possui um comportamento exploratório pseudo-aleatório que faz com que a tomada de decisão dinâmica do agente seja guiada, ora pelo perĄl de jogo do adversário, ora aleatoriamente. Contudo, ao conceber tal arquitetura, é necessário evitar o seguinte problema: devido às características inerentes à técnica de Elicitação Automática de Casos, nas fases iniciais do jogo Ű em que a quantidade de casos disponíveis na biblioteca é extremamente baixa em função do exíguo conhecimento do perĄl do adversário Ű a frequência de tomadas de decisão aleatórias seria muito elevada, o que comprometeria o desempenho do agente. Para atacar tal problema, este trabalho também propõe incorporar à arquitetura do ACE-RLCheckers um terceiro módulo, composto por uma base de regras de experiência extraída a partir de jogos de especialistas humanos, utilizando uma técnica de Mineração de Padrões Sequenciais. O objetivo de utilizar tal base é reĄnar e acelerar a adaptação do agente ao perĄl de seu adversário nas fases iniciais dos confrontos entre eles. Resultados experimentais conduzidos em torneio envolvendo ACE-RL-Checkers e outros agentes correlacionados com este trabalho, conĄrmam a superioridade da arquitetura dinâmica aqui proposta.
ake into account, in addition to the environment, the minimizing action of an opponent (such as in games), it is fundamental that the agent has the ability to progressively trace a proĄle of its adversary that aids it in the process of selecting appropriate actions. However, it would be unsuitable to construct an agent with a decision-making system based on only the elaboration of this proĄle, as this would prevent the agent from having its Şown identityŤ, which would leave it at the mercy of its opponent. Following this direction, this work proposes an automatic hybrid Checkers player, called ACE-RL-Checkers, equipped with a dynamic decision-making mechanism, which adapts to the proĄle of its opponent over the course of the game. In such a system, the action selection process (moves) is conducted through a composition of Multi-Layer Perceptron Neural Network and case library. In the case, Neural Network represents the ŞidentityŤ of the agent, i.e., it is an already trained static decision-making module and makes use of the Reinforcement Learning TD( ) techniques. On the other hand, the case library represents the dynamic decision-making module of the agent, which is generated by the Automatic Case Elicitation technique (a particular type of Case-Based Reasoning). This technique has a pseudo-random exploratory behavior, which makes the dynamic decision-making on the part of the agent to be directed, either by the game proĄle of the opponent or randomly. However, when devising such an architecture, it is necessary to avoid the following problem: due to the inherent characteristics of the Automatic Case Elicitation technique, in the game initial phases, in which the quantity of available cases in the library is extremely low due to low knowledge content concerning the proĄle of the adversary, the decisionmaking frequency for random decisions is extremely high, which would be detrimental to the performance of the agent. In order to attack this problem, this work also proposes to incorporate onto the ACE-RL-Checkers architecture a third module composed of a base of experience rules, extracted from games played by human experts, using a Sequential Pattern Mining technique. The objective behind using such a base is to reĄne and accelerate the adaptation of the agent to the proĄle of its opponent in the initial phases of their confrontations. Experimental results conducted in tournaments involving ACE-RL-Checkers and other agents correlated with this work, conĄrm the superiority of the dynamic architecture proposed herein.
Tese (Doutorado)
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(20390), Baolin Wu. "Fuzzy modelling and identification with genetic algorithms based learning." Thesis, 1996. https://figshare.com/articles/thesis/Fuzzy_modelling_and_identification_with_genetic_algorithms_based_learning/21345057.

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Modelling is an essential step towards a solution to complex system problems. Traditional mathematical methods are inadequate in describing the complex systems when the complexity increases. Fuzzy logic has provided an alternative way in dealing with complexity in real world.

This thesis looks at a practical approach for complex system modelling using fuzzy logic. This approach is usually called fuzzy modelling. The main aim of this thesis is to explore the capabilities of fuzzy logic in complex system modelling using available data. The fuzzy model concerned is the Sugeno-Takage-Kang model (TSK model). A genetic algorithm based learning algorithm (GABL) is proposed for fuzzy modelling. It basically contains four blocks, namely the partition, GA, tuning and termination blocks. The functioning of each block is described and the proposed algorithm is tested using a number of examples from different applications such as function approximation and processing control.

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Weng, Kuei-Sung, and 翁桂松. "Fuzzy Modeling Based on Genetic Ellipsoid Learning Algorithm." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/74814313454610298473.

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碩士
國立臺北科技大學
機電整合研究所
90
The theme of this thesis is to apply Genetic Algorithm (GA) and Gustafson-Kessel (G-K) Algorithm to the fuzzy modeling. A method called Genetic Ellipsoid Learning Algorithm (GELA) is proposed to learn the decision regions for pattern recognition and adaptive fuzzy modeling in this thesis. 1.First topics, a learning method based on fuzzy clustering and adaptively tuned hyperellipsoids is proposed to learn the decision regions for pattern recognition. The Gustafson- Kessel (G-K) algorithm for fuzzy clustering is modified in such a way that the Genetic Algorithm is applied to dynamically learn the volumes of hyperellipsoids in G-K algorithm. The decision regions are accurately learned by the proposed method in this paper so that on one hand, misclassification errors are minimized; on the other hand, the range of learned decision regions are not too wide to reduce the accuracy of pattern recognition. 2.Second topics, a method called Genetic Ellipsoid Learning Algorithm (GELA) is proposed for adaptive fuzzy modeling integrating Genetic Algorithm (GA) and Gustafson-Kessel (G-K) Algorithm. Since G-K algorithm is able to efficiently cover data points with multiple ellipsoids, GA is applied to estimate volume of each ellipsoid. Based on the volume learned by GA as well as input/output data points, G-K algorithm will then estimate the parameters of each ellipsoid. As input/output data points are clustered by multiple ellipsoids, a second GA is proposed to fine-tune the parameters of each ellipsoid for fuzzy modeling.
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Tzou, Tsung-Fei, and 鄒璁飛. "The Reinforcement Learning Behavior Unit Weights Searching based on Genetic Algorithm." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/67380234386530334498.

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碩士
國立中正大學
電機工程所
95
This thesis proposes a scheme based on Stochastic Searching Network and (GA) Genetic Algorithm, and we use Reinforcement Learning method for action network weights searching problem. The SGRL learning scheme is a hybrid Genetic Algorithm, which integrates the Stochastic Searching Network and the Genetic Algorithm to fulfill the Reinforcement Learning action network weights searching task. Structurally, the SGRL learning system is composed of two integrated feed-forward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the SGRL learning system, where the action network is a normal neural network. Using the TD (Temporal Difference) prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA and according to the plant dynamic reference　 model to adapt itself according to the internal reinforcement signal. The key concept of the SGRL learning scheme is to formulate the internal reinforcement signal contributed by the reference plant model as the fitness function for the GA. Computer simulations on controlling of the Acrobot (i.e. possessing fewer actuators than degrees of freedom) system and mountain-car system have been conducted to illustrate the performance and applicability of the proposed learning controller scheme.
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Book chapters on the topic "Genetic algorithm based learning algorithm (GABL)"

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Menéndez, Héctor, and David Camacho. "A Genetic Graph-Based Clustering Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2012, 216–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_27.

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Zhu, Kenny Q., and Ziwei Liu. "Population Diversity in Permutation-Based Genetic Algorithm." In Machine Learning: ECML 2004, 537–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_49.

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Tao, Jili, Ridong Zhang, and Yong Zhu. "Further Idea on Optimal Q-Learning Fuzzy Energy Controller for FC/SC HEV." In DNA Computing Based Genetic Algorithm, 261–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2_10.

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Chandra Shekar, K., Priti Chandra, and K. Venugopala Rao. "Relative-Feature Learning through Genetic-Based Algorithm." In Proceedings of the Second International Conference on Computational Intelligence and Informatics, 69–79. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8228-3_8.

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Barka, Kamel, Lyamine Guezouli, Samir Gourdache, and Sara Ameghchouche. "Mobility Based Genetic Algorithm for Heterogeneous Wireless Networks." In Machine Learning for Networking, 93–106. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70866-5_6.

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El-Shorbagy, M. A., A. Y. Ayoub, I. M. El-Desoky, and A. A. Mousa. "A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 92–101. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_10.

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Liu, Kai, and Jin Tian. "Subspace Learning with an Archive-Based Genetic Algorithm." In Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018, 181–88. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3402-3_20.

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Alexander, Vimala, and Pethalakshmi Annamalai. "An Elitist Genetic Algorithm Based Extreme Learning Machine." In Advances in Intelligent Systems and Computing, 301–9. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0251-9_29.

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Zhu, Chen, and Jing Liu. "A Direction based Multi-Objective Agent Genetic Algorithm." In Intelligent Data Engineering and Automated Learning – IDEAL 2013, 210–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41278-3_26.

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Li, Bin, and Zhen-quan Zhuang. "Genetic Algorithm Based-On the Quantum Probability Representation." In Intelligent Data Engineering and Automated Learning — IDEAL 2002, 500–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_75.

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Conference papers on the topic "Genetic algorithm based learning algorithm (GABL)"

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Yichen, Liu, Li Bo, Zhao Chenqian, and Ma Teng. "Intelligent Frequency Assignment Algorithm Based on Hybrid Genetic Algorithm." In 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00-50.

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Dong, Li-yan, Guang-yuan Liu, Sen-miao Yuan, Yong-li Li, and Zhen Li. "Classifier Learning Algorithm Based on Genetic Algorithms." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.214.

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Li, Mingwei, Na Qin, Tao Zhu, Yongjie Mao, and Jiaxi Zhao. "Carrier Aircraft Scheduling Optimization Based on A* Algorithm and Genetic Algorithm." In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022. http://dx.doi.org/10.1109/ddcls55054.2022.9858589.

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Fa-Chao Li, Lian-Qing Su, and Hai-Chao Ran. "The fuzzy genetic algorithm based on rule." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527356.

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Jiu-Ling Zhao, Jiu-Fen Zhao, and Jian-Jun Li. "Intrusion detection based on clustering genetic algorithm." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527621.

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Zhang, Lifeng, Qiuxuan Wu, Xiaoni Chi, Jian Wang, Botao Zhang, Weijie Lin, Sergey A. Chepinskiy, Anton A. Zhilenkov, Yanbin Luo, and Farong Gao. "RNA genetic algorithm based on octopus learning mechanism." In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2021. http://dx.doi.org/10.1109/rcar52367.2021.9517596.

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Liu, Hai, Bin Jiao, Long Peng, and Ting Zhang. "Extreme learning machine based on improved genetic algorithm." In 5th International Conference on Information Engineering for Mechanics and Materials. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icimm-15.2015.38.

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Semenikhin, S. V., and L. A. Denisova. "Learning to rank based on modified genetic algorithm." In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2016. http://dx.doi.org/10.1109/dynamics.2016.7819080.

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Hirchoua, Badr, Imadeddine Mountasser, Brahim Ouhbi, and Bouchra Frikh. "Evolutionary Deep Reinforcement Learning Environment: Transfer Learning-Based Genetic Algorithm." In iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487664.3487698.

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Jing-Kai Li, Jian Chen, and Hua-Qing Min. "A classification method based on Immune Genetic Algorithm." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359531.

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Reports on the topic "Genetic algorithm based learning algorithm (GABL)"

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TEACHING-LEARNING BASED OPTIMIZATION METHOD CONSIDERING BUCKLING AND SLENDERNESS RESTRICTION FOR SPACE TRUSSES. The Hong Kong Institute of Steel Construction, March 2022. http://dx.doi.org/10.18057/ijasc.2022.18.1.3.

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The structural performance of a building is a function of several parameters and constraints whose association may offer non unique solutions which, however, meet the design requirements. Therefore, an optimization routine is needed to determine the best solution within the set of available alternatives. In this study, the TLBO method was implemented for weight-based optimization of space trusses. The algorithm applies restrictions related to the critical buckling load as well as the slenderness ratio, which are the basis to obtain reliable and realistic results. To assess the capability of the TLBO method, two reference cases and a transmission tower are subjected to the optimization analysis. In the transmission tower analysis, however, a more realistic approach is adopted as it also considers, through a safety factor, the plastic behavior in the critical buckling load constraint. With no optimization, the ideal weight increases by 101.36% when the critical buckling load is considered in the first two cases, which is consistent with the expected behavior. If the slenderness of the elements is also restricted, the ideal weight now rises by 300.78% from the original case and by 99.04% from the case where only the critical buckling load restriction is applied. Now, considering the critical buckling load and slenderness restriction with the TLBO method applied, a 18.28% reduction in the ideal weight is verified. In fact, the proposed optimization procedure converged to a better solution than that of the reference study, which is based on the genetic algorithms method.
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The clustering algorithm can complete the classification without labeled data, but it is limited by the low accuracy. In this paper, the optimized VMD is used to decompose and standardize the vibration signal. Then the correlation-based feature selection method is implemented to obtain the feature results after dimensionality reduction. Finally, the results are input into the classifier combined by K-means and genetic algorithm (GA). By introducing and optimizing the genetic algorithm, the number of classes can be selected automatically, and the accuracy is significantly improved. This method can carry out adaptive multiple fault detection of a diesel engine without labeled data. Compared with many supervised learning algorithms, the proposed method also has high accuracy.
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