Relevant bibliographies by topics / Genetic algorithm basedselected ensemble

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  2. Dissertations / Theses
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Academic literature on the topic 'Genetic algorithm basedselected ensemble'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Genetic algorithm basedselected ensemble"

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Min, Sung-Hwan. "Genetic Algorithm based Hybrid Ensemble Model." Journal of Information Technology Applications and Management 23, no. 1 (2016): 45–59. http://dx.doi.org/10.21219/jitam.2016.23.1.045.

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Lee, Seogyoung, Martin Seunghwan Yang, Jongkyeong Kang, and Seung Jun Shin. "Ensemble variable selection using genetic algorithm." Communications for Statistical Applications and Methods 29, no. 6 (2022): 629–40. http://dx.doi.org/10.29220/csam.2022.29.6.629.

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Watanabe, Ryuya, and Lei Li. "Generation of Sparse AutoEncoder and Ensemble Learning Based on the Genetic Algorithm." Information 27, no. 2 (2024): 111–29. http://dx.doi.org/10.47880/inf2702-02.

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In this paper, we discuss optimization of parameters for generation of sparse AutoEncoder based on the genetic algorithm, and show an efficient ensemble learning algorithm. From experiment result of some data sets, we generated better sparse AutoEncoder, and get ensemble effect from the diversity of data in the middle layer of AutoEncoder. Keywords: Sparse AutoEncoder, Ensemble Learning, Genetic Algorithm
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Kim, Young-Won, and Il-Seok Oh. "Hybrid Genetic Algorithm for Classifier Ensemble Selection." KIPS Transactions:PartB 14B, no. 5 (2007): 369–76. http://dx.doi.org/10.3745/kipstb.2007.14-b.5.369.

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Reza Ghaemi. "Pattern Ensemble Learning Method for Clustering Ensemble using Incremental Genetic-Based Algorithm." Power System Technology 49, no. 1 (2025): 24–52. https://doi.org/10.52783/pst.1389.

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The clustering ensemble has emerged as a prominent method for improving clustering accuracy of unsupervised classification. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper has proposed an Incremental Genetic-Based Algorithm for Clustering Ensemble (IGCE) to perform the search task, but has replaced its traditional crossover operator with a Pattern Ensemble Learning Method (PEL). Therefore, IGCE-PEL is capable to avoid the problems of clustering invalidity and context insensitivity from the traditional crossover operator of genetic algorithms. IGCEs have been evaluated on twelve benchmark datasets based on different recombination operators used. The experimental results have demonstrated that IGCE using PEL is able to achieve better clustering accuracy when compared with several other existing genetic-based clustering ensemble algorithms.
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Wang, Yanhua, Xiyu Liu, and Laisheng Xiang. "GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4367342.

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Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.
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Das, Asit K., Sunanda Das, and Arka Ghosh. "Ensemble feature selection using bi-objective genetic algorithm." Knowledge-Based Systems 123 (May 2017): 116–27. http://dx.doi.org/10.1016/j.knosys.2017.02.013.

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Chatterjee, Sujoy, and Anirban Mukhopadhyay. "Clustering Ensemble: A Multiobjective Genetic Algorithm based Approach." Procedia Technology 10 (2013): 443–49. http://dx.doi.org/10.1016/j.protcy.2013.12.381.

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Oh, Dong-Yop, and J. Brian Gray. "GA-Ensemble: a genetic algorithm for robust ensembles." Computational Statistics 28, no. 5 (2013): 2333–47. http://dx.doi.org/10.1007/s00180-013-0409-6.

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KIM, YOUNG-WON, and IL-SEOK OH. "COARSE-TO-FINE CLASSIFIER ENSEMBLE SELECTION USING CLUSTERING AND GENETIC ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1083–106. http://dx.doi.org/10.1142/s021800140900751x.

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A good classifier ensemble should show high complementarity among classifiers to produce a high recognition rate and it should also have a small size to be efficient. This paper proposes a classifier ensemble selection algorithm operating in a coarse-to-fine paradigm. For the algorithm to be successful, the original classifier pool should be sufficiently diverse. So this paper produces a large classifier pool by combining several different classification algorithms and several feature subsets. The coarse selection stage reduces greatly the size of the classifier pool using a clustering algorithm. The fine selection finds the near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation used handwritten numeral datasets and UCI datasets. The experimental results and the test of statistical significance showed that the proposed algorithm is superior to the conventional ones.
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Dissertations / Theses on the topic "Genetic algorithm basedselected ensemble"

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Ferreira, Ednaldo José. ""Abordagem genética para seleção de um conjunto reduzido de características para construção de ensembles de redes neurais: aplicação à língua eletrônica"." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18052006-143603/.

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As características irrelevantes, presentes em bases de dados de diversos domínios, deterioram a acurácia de predição de classificadores induzidos por algoritmos de aprendizado de máquina. As bases de dados geradas por uma língua eletrônica são exemplos típicos onde a demasiada quantidade de características irrelevantes e redundantes prejudicam a acurácia dos classificadores induzidos. Para lidar com este problema, duas abordagens podem ser utilizadas. A primeira é a utilização de métodos para seleção de subconjuntos de características. A segunda abordagem é por meio de ensemble de classificadores. Um ensemble deve ser constituído por classificadores diversos e acurados. Uma forma efetiva para construção de ensembles de classificadores é por meio de seleção de características. A seleção de características para ensemble tem o objetivo adicional de encontrar subconjuntos de características que promovam acurácia e diversidade de predição nos classificadores do ensemble. Algoritmos genéticos são técnicas promissoras para seleção de características para ensemble. No entanto, a busca genética, assim como outras estratégias de busca, geralmente visam somente a construção do ensemble, permitindo que todas as características (relevantes, irrelevantes e redundantes) sejam utilizadas. Este trabalho apresenta uma abordagem baseada em algoritmos genéticos para construção de ensembles de redes neurais artificiais com um conjunto reduzido das características totais. Para melhorar a acurácia dos ensembles, duas abordagens diferenciadas para treinamento de redes neurais foram utilizadas. A primeira baseada na interrupção precoce do treinamento com o algoritmo back-propagation e a segunda baseada em otimização multi-objetivo. Os resultados obtidos comprovam a eficácia do algoritmo proposto para construção de ensembles de redes neurais acurados. Também foi constatada sua eficiência na redução das características totais, comprovando que o algoritmo proposto é capaz de construir um ensemble utilizando um conjunto reduzido de características.<br>The irrelevant features in databases of some domains spoil the accuracy of the classifiers induced by machine learning algorithms. Databases generated by an electronic tongue are examples where the huge quantity of irrelevant and redundant features spoils the accuracy of classifiers. There are basically two approaches to deal with this problem: feature subset selection and ensemble of classifiers. A good ensemble is composed by accurate and diverse classifiers. An effective way to construct ensembles of classifiers is to make it through feature selection. The ensemble feature selection has an additional objective: to find feature subsets to promote accuracy and diversity in the ensemble of classifiers. Genetic algorithms are promising techniques for ensemble feature selection. However, genetic search, as well as other search strategies, only aims the ensemble construction, allowing the selection of all features (relevant, irrelevant and redundant). This work proposes an approach based on genetic algorithm to construct ensembles of neural networks using a reduced feature subset of totality. Two approaches were used to train neural networks to improve the ensembles accuracy. The first is based on early stopping with back-propagation algorithm and the second is based on multi-objective optimization. The results show the effectiveness and accuracy of the proposed algorithm to construct ensembles of neural networks, and also, its efficiency in the reduction of total features was evidenced, proving its capacity for constructing an ensemble using a reduced feature subset.
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Jannot, Xavier. "Modélisation et optimisation d’un ensemble convertisseur-machine. Application aux systèmes d’entrainement à haute vitesse." Thesis, Supélec, 2010. http://www.theses.fr/2010SUPL0004/document.

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Les travaux présentés dans cette thèse concernent la modélisation et l’optimisation d’un ensemble Convertisseur-Machine devant fonctionner à haute vitesse. La première partie établit un état de l’art des méthodologies de conception relatives au dimensionnement optimal de systèmes d’entrainement et analyse les particularités du fonctionnement à haute vitesse. Puis, une modélisation analytique multiphysique des éléments du système est réalisée. Afin de mener une conception globalement optimale, les interactions significatives entre les éléments du système doivent être modélisées. Cela est effectué à l’aide d’une modélisation électrique fine – qui est le cœur de la caractérisation des interactions – mettant en œuvre une approche harmonique originale. Il en découle alors une modélisation des interdépendances entre onduleur et machine au niveau des pertes dans le système et de la qualité du couple. La modélisation est ensuite couplée à un algorithme génétique selon une méthodologie de conception hybride faisant intervenir des modèles analytiques puis par éléments finis. Enfin, cette démarche est appliquée au dimensionnement de deux systèmes d’entrainement dont le moteur est une MSAP à aimantation orthoradiale. Ces deux cas d’application ont été traités avec succès et ont mis en avant l’intérêt d’une approche « Système » dans la conception de systèmes d’entrainement. Par ailleurs nous avons analysé les morphologies des machines en fonction de la vitesse de rotation. Ceci a fait ressortir des capacités intéressantes de ce type de machines – pour la haute vitesse – d’un point de vue magnétique, mécanique, et des pertes au rotor<br>The work presented in this thesis aims at the modelling and optimisation of a set Converter-Machine that is intended to run at high speeds. The first part is a state of the art of the conception methodologies related to the optimal design of drives and investigates the particularities of high speed operations. Then, an analytic and multiphysic modelling of the elements of the system is performed. But, in order to carry out a global optimal conception, the significant interactions between the various elements of the system must be modelled. This is achieved through a precise electrical model – that is the core of the characterization of the interactions – involving an original harmonic approach. From this follows a modelling of the interdependences between inverter and machine in the system losses and in the torque quality. The models are then associated with a genetic algorithm according to a hybrid methodology of conception involving analytical and finite-element models. Finally, this procedure is applied to the design of two drives of which the motor is a PMSM with circumferentially magnetised magnets. These two cases have been successfully handled and clearly show the assets of a “System” approach for the design of drives. In addition, the morphologies of the optimal machines are analysed according to their rotation speed. This analysis highlights some interesting abilities of this kind of machines – in high speed – regarding the magnetic and mechanical behaviours, and the rotor losses
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Thames, John Lane. "Advancing cyber security with a semantic path merger packet classification algorithm." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45872.

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This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theoretical aspects of the semantic path merger packet classification algorithm are investigated, and the algorithm's hardware-based implementation is evaluated along with comparative analysis versus content addressable memory. Experimental results show that the hardware implementation of the semantic path merger algorithm significantly outperforms content addressable memory in terms of energy consumption and operational timing.
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Muñoz, Mas Rafael. "Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/76168.

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This dissertation focused in the comprehensive analysis of the capabilities of some non-tested types of Artificial Neural Networks, specifically: the Probabilistic Neural Networks (PNN) and the Multi-Layer Perceptron (MLP) Ensembles. The analysis of the capabilities of these techniques was performed using the native brown trout (Salmo trutta; Linnaeus, 1758), the bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) and the redfin barbel (Barbus haasi; Mertens, 1925) as target species. The analyses focused in the predictive capabilities, the interpretability of the models and the effect of the excess of zeros in the training datasets, which for presence-absence models is directly related to the concept of data prevalence (i.e. proportion of presence instances in the training dataset). Finally, the effect of the spatial scale (i.e. micro-scale or microhabitat scale and meso-scale) in the habitat suitability models and consequently in the e-flow assessment was studied in the last chapter.<br>Esta tesis se centra en el análisis comprensivo de las capacidades de algunos tipos de Red Neuronal Artificial aún no testados: las Redes Neuronales Probabilísticas (PNN) y los Conjuntos de Perceptrones Multicapa (MLP Ensembles). Los análisis sobre las capacidades de estas técnicas se desarrollaron utilizando la trucha común (Salmo trutta; Linnaeus, 1758), la bermejuela (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) y el barbo colirrojo (Barbus haasi; Mertens, 1925) como especies nativas objetivo. Los análisis se centraron en la capacidad de predicción, la interpretabilidad de los modelos y el efecto del exceso de ceros en las bases de datos de entrenamiento, la así llamada prevalencia de los datos (i.e. la proporción de casos de presencia sobre el conjunto total). Finalmente, el efecto de la escala (micro-escala o escala de microhábitat y meso-escala) en los modelos de idoneidad del hábitat y consecuentemente en la evaluación de caudales ambientales se estudió en el último capítulo.<br>Aquesta tesis se centra en l'anàlisi comprensiu de les capacitats d'alguns tipus de Xarxa Neuronal Artificial que encara no han estat testats: les Xarxes Neuronal Probabilístiques (PNN) i els Conjunts de Perceptrons Multicapa (MLP Ensembles). Les anàlisis sobre les capacitats d'aquestes tècniques es varen desenvolupar emprant la truita comuna (Salmo trutta; Linnaeus, 1758), la madrilla roja (Achondrostoma arcasii; Robalo, Almada, Levy & Doadrio, 2006) i el barb cua-roig (Barbus haasi; Mertens, 1925) com a especies objecte d'estudi. Les anàlisi se centraren en la capacitat predictiva, interpretabilitat dels models i en l'efecte de l'excés de zeros a la base de dades d'entrenament, l'anomenada prevalença de les dades (i.e. la proporció de casos de presència sobre el conjunt total). Finalment, l'efecte de la escala (micro-escala o microhàbitat i meso-escala) en els models d'idoneïtat de l'hàbitat i conseqüentment en l'avaluació de cabals ambientals es va estudiar a l'últim capítol.<br>Muñoz Mas, R. (2016). Multivariate approaches in species distribution modelling: Application to native fish species in Mediterranean Rivers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/76168<br>TESIS
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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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Hou, Huai En, and 侯懷恩. "Ensemble Classifier Using Genetic Algorithm Approach." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/18507611141828880269.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>100<br>In past research, generate a lot of information classification and prediction techniques, such as BPN、DT、NB、KNN、SVM Are currently commonly used method. These methods have their advantages and disadvantages, each classifier has its own advantages and disadvantages and applicable problem characteristics, scholars began to study the combination of many of the classifier to produce a better classification results, this method is called the ensemble. Previous studies have never used the classifier the attribute selection combined with the ensemble to do the classification prediction. The purpose of this study is to use Genetic Algorithm coupled with Ensemble Common Bagging、Adaboost and classifier(BPN、DT、NB、KNN、SVM) conduct experimental tests to assess the classification accuracy rate performance. Experimental results show use Genetic Algorithm coupled with Ensemble classifier than did not use Genetic Algorithm coupled with Ensemble classifier and use only Genetic Algorithm for attribute selection classifier classification accuracy is better, the results of the test also showed significant differences.
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Chen, Su-Hsuan, and 陳俗玄. "Using Genetic Algorithm to Optimize the Diversity of Classifier Ensemble." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/57499181493790814406.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>100<br>Data classification method is one of the main tasks of data mining. In the literature, there are many classic base inducers used to train the classifier such as decision tree, neural network…etc., which are all individual classifier. In the past few years, many researches have proposed that the classifier ensemble, which composed by more than one individual classifier, is more effective than any individual classifier of the classifier ensemble. The main idea for classifier ensemble to classify a new sample is to combine the output of each individual classifier and then reach the final decision. Therefore, the diversity between the classifiers is considered as an important factor in classification accuracy. Because there are few literatures to research about how to optimize the diversity, this paper would propose an ensemble method（Diversity by evolutionary computing resampling training subset, DECRTS）that uses the genetic algorithm to encourage the diversity between classifiers by manipulating the train data set. We design an experiment using 21 UCI Repository of machine learning databases to test and verify and then comparing with individual classifier and other classifier ensembles. The result provides that the DECRTS in our experiment has better average accuracy（82.19%）and is significantly difference with other method except Adaboost（81.99%）. Moreover, the experiment appears the different method to create diversity sometimes would have better performance in particular datasets.
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Haque, Mohammad Nazmul. "Genetic algorithm-based ensemble methods for large-scale biological data classification." Thesis, 2017. http://hdl.handle.net/1959.13/1335393.

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Research Doctorate - Doctor of Philosophy (PhD)<br>We study the search for the best ensemble combinations from the wide variety of heterogeneous base classifiers. The number of possible ways to create the ensemble with a large number of base classifiers is exponential to the base classifiers pool size. To search for the best combinations from that wide search space is not suitable for exhaustive search because of it's exponential growth with the ensemble size. Hence, we employed a genetic algorithm to find the best ensemble combinations from a pool of heterogeneous base classifiers. The classification decisions of base classifiers are combined using the popular majority vote approach. We used random sub-sampling for balancing the class distributions in the class-imbalanced datasets. The empirical result on benchmarking and real-world datasets apparently outperformed the performances of base classifiers and other state-of-the-art ensemble methods. Afterwards, we evaluated the performance of an ensemble of classifiers combination search in a weighted voting approach using the differential evolution (DE) algorithm to find if employing weights could increase the generalisation performances of ensembles. The weights optimised by DE also outperformed both of the base classifiers and other ensembles for benchmarking and real-world biological datasets. Finally, we extend the majority voting-based ensemble of classifiers combination search with multi-objective settings. The search space is spread over the all possible ensemble combinations created with 29 heterogeneous base classifiers and the selection of feature subset from six feature selection methods as wrapper approach. The optimisation of two objectives, the maximisation of training MCC scores and maximisation of the diversity among base classifiers, with NSGA-II, a popular multi-objective genetic algorithm, is used for simultaneously finding the best feature set and the ensemble combinations. We analyse the Pareto front of solutions obtained by NSGA-II for their generalisation performances. Datasets taken from UCI machine learning repository and NIPS2003 feature selection challenges have been used to investigate the performance of proposed method. The experimental outcomes suggest that the proposed multiobjective-based NSGA-II found the better feature set and the best ensemble combination that produces better generalisation performances in compared to other ensemble of classifiers methods.
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Lin, Tse-Yen, and 林澤岩. "Development of a Real Time Scheduling System by Genetic Algorithm with Ensemble." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/62229464082478365177.

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碩士<br>華梵大學<br>資訊管理學系碩士班<br>97<br>On the basis of earlier study results have found that if the constrtlction of the machinelearning based RTS system knowledge base (KB) results in a scheduling KB with bettergeneralization ability (i.e., classification accuracy rate); the KB also leads to superiorproduction performance with respect to the RTS system KBs with bad generalization ability.Besides, there are two issues that significantly affect the generalization ability of machinelearning-based classifiers: the issue of feature subset selection (including parameteroptimization) and ensemble of the classifiers. The fealure subset selection issue results fi'om the Ihct that a data set usually containshundreds of features, many of which are irrelevant and heavily correlated with others.Without feature selection, they tend to deteriorate perlbrmance of the model, as well asincrease the model training time. Feature subset selection can be t~3rmulated as anoptimization problem which involves searching the space of possible features to identify asubset that is optimum or near-optimal with respect to classification accuracy rate. Ensemblecombines the predictions of individual classifiers with the equal weight or weights based onestimated prediction accuracy; earlier study indicated that ensemble models have demonstrated consistent in some cases, remarkable improvements in prediction accuracy overindividual classifiers. This researchwill develop GA-based wrapper feature selection andparameter optimization with three different machine learning algorithms (BPNN, DT, and SVM) as a base learner and then uses majority voting scheme that ensemble threeclassification algorithms denoted as GA+Voting classifiers. The performance of this thesis proposed ensemble of the classifiers based on GA wrapper approach will be compared using datasets in FMS model (i.e., RTS system on line simulation experiment verification) to those of other machine learning-based classifiers (GA+BPNN, GA+DT, and GA+SVM) to demonstrate the proposed approaches superiority.
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Lin, Jheng-Hong, and 林政弘. "Financial Time Series Forecasting Using Ensemble Empirical Decomposition Mode, Genetic Algorithm and Extreme Learning Machine." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/mf36zg.

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碩士<br>國立臺北科技大學<br>商業自動化與管理研究所<br>99<br>Financial time series are inherently nonlinear and non-stationary, it is therefore difficult using statistical models to forecast. ANN(Artificial Neural Networks)does not require strict theoretical assumptions, so it has been widely applied for financial prediction. On ANN learning algorithms, the ELM(Extreme Learning Machine) overcomes the drawback of traditional Back-propagation. This study takes the closing price of Taiwan Capitalization Weighted Stock Index, Shanghai Stock Exchange Composite Index and Hong Kong Hang Seng Index as research subjects during the period of 2001 to 2010. We propose a hybrid forecasting model based on EEMD(Ensemble Empirical Mode Decomposition), GA (Genetic Algorithm) and ELM. Firstly, by using EEMD to decompose stock price into several IMF(Intrinsic Mode Functions) and each IMF component is modeled by individual EELM respectively. Then, we find the optimal parameters with GA. In order to examine the proposed models are better than traditional statistical models, these four models also compare with the ARIMA model. The study concluded the model combined with ELM, GA, EEMD, which has the best prediction performance. The performance of proposed four models is better than ARIMA models, showing the excellence of proposed models.
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Book chapters on the topic "Genetic algorithm basedselected ensemble"

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Patel, Rahila, M. M. Raghuwanshi, and L. G. Malik. "Ensemble of Dying Strategies Based Multi-objective Genetic Algorithm." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03753-0_44.

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Shen, Zhang-Quan, and Fan-Sheng Kong. "Optimizing Weights by Genetic Algorithm for Neural Network Ensemble." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_55.

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Ghorpade-Aher, Jayshree, and Balwant Sonkamble. "Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm." In Proceedings of Sixth International Congress on Information and Communication Technology. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2380-6_32.

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Han, Kate, Tien Pham, Trung Hieu Vu, Truong Dang, John McCall, and Tien Thanh Nguyen. "VEGAS: A Variable Length-Based Genetic Algorithm for Ensemble Selection in Deep Ensemble Learning." In Intelligent Information and Database Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73280-6_14.

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Santana, Laura E. A., and Anne M. P. Canuto. "Bi-objective Genetic Algorithm for Feature Selection in Ensemble Systems." In Artificial Neural Networks and Machine Learning – ICANN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33269-2_88.

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Zhou, Shude, and Zengqi Sun. "Using Ensemble Method to Improve the Performance of Genetic Algorithm." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_36.

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Mahmood, Amjad, Tianrui Li, Yan Yang, and Hongjun Wang. "Semi-supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6_1.

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Padilha, Carlos, Adrião Dória Neto, and Jorge Melo. "Random Subspace Method and Genetic Algorithm Applied to a LS-SVM Ensemble." In Artificial Neural Networks and Machine Learning – ICANN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_21.

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Tao, Hui, Xiao-ping Ma, and Mei-ying Qiao. "Support Vector Machine Selective Ensemble Learning on Feature Clustering and Genetic Algorithm." In Electrical, Information Engineering and Mechatronics 2011. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_193.

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Kim, Kyung-Joong, and Sung-Bae Cho. "DNA Gene Expression Classification with Ensemble Classifiers Optimized by Speciated Genetic Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11590316_104.

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Conference papers on the topic "Genetic algorithm basedselected ensemble"

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Han, Kate, Truong Thanh Nguyen, Viet Anh Vu, Alan Wee-Chung Liew, Truong Dang, and Tien Thanh Nguyen. "VISTA: A Variable Length Genetic Algorithm and LSTM-Based Surrogate Assisted Ensemble Selection algorithm in Multiple Layers Ensemble System." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612029.

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Saiyed, Makhduma F., and Irfan Al-Anbagi. "Optimized Ensemble Model with Genetic Algorithm for DDoS Attack Detection in IoT Networks." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615607.

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Afroz, Tamanna, and Tazwar Mohammed Shoumik. "Optimizing Energy Efficiency through Explainable AI: A Genetic Algorithm-Optimized Ensemble Method for Improved Decision-Making." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022503.

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Rasul, Mohamed J. M. A., Faiz Majeed, Dania Batool, et al. "Adaptive Stacking Ensemble Model with Genetic Algorithm-driven Hyperparameter Optimization for State of Health Prediction of Lithium-ion Batteries." In 2024 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2024. https://doi.org/10.1109/ecce55643.2024.10861773.

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Fida, Benish, Muhammad Nazir, Nawazish Naveed, and Sheeraz Akram. "Heart disease classification ensemble optimization using Genetic algorithm." In 2011 IEEE 14th International Multitopic Conference (INMIC). IEEE, 2011. http://dx.doi.org/10.1109/inmic.2011.6151471.

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Wang, Gang, Xinshun Xu, and Liang Peng. "Genetic Algorithm Based Selective Ensemble with Multiset Representation." In 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI). IEEE, 2010. http://dx.doi.org/10.1109/aici.2010.91.

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Abolkarlou, Niloofar Afshari, Ali Akbar Niknafs, and Mohammad Kazem Ebrahimpour. "Ensemble imbalance classification: Using data preprocessing, clustering algorithm and genetic algorithm." In 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2014. http://dx.doi.org/10.1109/iccke.2014.6993364.

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Mohammadi, Moslem, Hosein Alizadeh, and Behrouz Minaei-Bidgoli. "Neural Network Ensembles Using Clustering Ensemble and Genetic Algorithm." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.329.

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Fayyazifar, Najmeh, and Najmeh Samadiani. "Parkinson's disease detection using ensemble techniques and genetic algorithm." In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017. http://dx.doi.org/10.1109/aisp.2017.8324074.

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Mehmood, Yasir, Muhammad Ishtiaq, Muhammad Tariq, and M. Arfan Jaffar. "Classifier ensemble optimization for gender classification using Genetic Algorithm." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625731.

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Reports on the topic "Genetic algorithm basedselected ensemble"

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Rostami, Omid. Industry Superstars: Unmasking Key Features that Drive Firm-Level Performance in Chinese Markets Using Ensemble Learning with Genetic Algorithm. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-118.

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