Self-configuring genetic programming algorithm with modified uniform crossover

Semenkin, ‪Eugene; Semenkina, Maria

doi:10.1109/cec.2012.6256587

Cited by 28 publications

(12 citation statements)

References 11 publications

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“…For solving the problem of GA setting parameters we use the self-configuring algorithm that was proposed in [17]. The scheme of the self-configuring GA is presented in Figure 1.…”

Section: Self-adaptive Gamentioning

confidence: 99%

“…Parameters combination selection at random can be also insufficient as algorithm efficiency on same problem can differ very much for various parameters setting. This problem can be solved with self-configuring GA [17] or coevolutionary GA [19]. Therefore, we propose a use of self-adaptive GA for the feature selection in the field of natural language call routing.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

Коромыслова

Semenkina

Sergienko

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract:The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms. IntroductionNatural language call routing is an important problem in the design of modern automatic call services and the solving of this problem could lead to improvement of the call service [21]. Generally natural language call routing can be considered as two different problems. The first one is speech recognition of calls and the second one is topic categorization of users utterances for further routing. Topic categorization of users utterances can be also useful for multidomain spoken dialogue system design [12]. In this work we treat call routing as an example of a text classification application.In the vector space model [16] text classification is considered as a machine learning problem. The complexity of text categorization with a vector space model is compounded by the need to extract the numerical data from text information before applying machine learning algorithms. Therefore, text classification consists of two parts: text preprocessing and classification algorithm application using the obtained numerical data. Text preprocessing comprises three stages:-Textual feature extraction.-Term weighting -Dimensionality reduction. The first one is the textual feature extraction based on raw preprocessing of the documents. This process includes deleting punctuation, transforming capital letters to lowercase, and additional procedures such as stop-words filtering [4] and stemming [14]. Stop-words list contains pronouns, prepositions, articles and other words that usually have no importance for the classification. Using stemming it is possible to join different forms of the same word into one textual feature.The second stage is the numerical feature extraction based on term weighting. For term weighting we use "bag-of-words" model, in which the word order is ignored. There exist different unsupervised and supervised term weighting methods. The most well-known unsupervised term weighting method is TFIDF [15]. The following supervised term weighting methods are also considered in the paper:

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“…For solving the problem of GA setting parameters we use the self-configuring algorithm that was proposed in [17]. The scheme of the self-configuring GA is presented in Figure 1.…”

Section: Self-adaptive Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

Коромыслова

Semenkina

Sergienko

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…In the proposed GA-based optimization, we use binary encoding, roulette-wheel parent selection, uniform crossover [29][30][31], and one-point mutation. In this study, we fix the maximum number of generations at 500.…”

Section: Hybrid Feature Extractionmentioning

confidence: 99%

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

2016

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Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors. This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method. The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and ak-NN average classification accuracy-based wrapper analysis approach that selects the most optimal features. The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature. The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions. The effectiveness of the proposed feature selection scheme is verified through an online process that diagnoses faults in an unknown AE fault signal by extracting only the selected features and using thek-NN classification algorithm to classify the fault condition manifested in the unknown signal. The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches. Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.

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“…The applied self-configuration method is based on the idea of encouraging those operators which received the highest total fitness in the current generation [8,9]. This approach has proved its efficiency in the solving of hard real-world optimization problems [10,11] and has been recommended for practical use.…”

Section: Description Of the Optimization Techniquementioning

confidence: 99%

Application of Self-Gonfiguring Genetic Algorithm for Human Resource Management

ˇSkraba¹,

Kofjaˇc²,

ˇZnidarˇsiˇc³

et al. 2015

J. Sib. Fed. Univ., Math. phys.

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This paper describes the problem of human resource management which can appear in many organizations during restructuration periods. The problem is simulated by a dynamic model, similar to a supply chain model with several ranks. The problem of finding the optimal combination of transition coefficients, including the fluctuation coefficients, is transformed into an optimization problem. To solve this problem, a self-configuring genetic algorithm is applied with several constraint handling methods. Additional constraints are defined in order to avoid undesirable oscillations in the system. The results show that this problem can be efficiently solved by the presented methods.

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Self-configuring genetic programming algorithm with modified uniform crossover

Cited by 28 publications

References 11 publications

Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

Application of Self-Gonfiguring Genetic Algorithm for Human Resource Management

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