Hybrid Model Of Genetic Algorithm Research Articles

Evaluation of the hybrid method of genetic algorithm and adaptive neural-fuzzy network (ANFIS) model in predicting the bankruptcy of companies listed on the Tehran stock exchange

One of the most important issues in financial, economic, and accounting matters is the phenomenon of bankruptcy and its prediction. There is presented a hybrid method of genetic algorithm and adaptive neural-fuzzy network model (ANFIS) to evaluate predicting the bankruptcy of companies listed on the Tehran Stock Exchange. The statistical population of this research is the successful and bankrupt manufacturing companies in Tehran Stock Exchange and in this research, there is a different way as opposed to previous and purposeful research and all companies can prevent their possible bankruptcy with accurate forecasting. In this way, the statistical population includes 136 companies consisting of bankrupt and non-bankrupt companies. In order to construct prediction models, four variables were first selected: 1. Independent sample t-test, 2. Correlation matrix (CM), 3. Step-by-step diagnostic analysis (SDA) and 4. Principal component analysis (PCA). The final financial ratios were selected from 19 financial ratios that using selected financial ratios and a hybrid model of ANFIS and genetic algorithm and the results of the proposed model and its comparison with the hybrid model of genetic algorithm and group method of data handling shows the high capability of the proposed GA-ANFIS model in bankruptcy prediction modeling and its superiority over GA-GMDH method. The results also show that the CM-GA-ANFIS model is known as the best model for predicting bankruptcy of companies listed on the Tehran Stock Exchange. The main reason for choosing the model (GA-ANFIS) is that in addition to the fact that for the first time a combination of two methods ANFIS and genetic algorithm is used to predict the bankruptcy of companies, and also in none of the studies conducted in both areas which further highlights the need for the present study.

An ANN-based ensemble model for change point estimation in control charts

Signaling in the control charts is usually followed by a substantial amount of delay, in which precise identification of the time when a change has occurred in a process simplifies the removal of change causes. This problem is referred to as change point (CP) estimation in the literature. This paper proposes a novel ensemble model to estimate CP under different processes’ changes in the phase II applications, known as ANNCP. It uses an evolutionary artificial neural network (ANN) as an underlying reasoning scheme to combine the predictions of multiple techniques and make the final decision. Specifically, a hybrid model of genetic algorithm (GA) and simulated annealing (SAN) with a new loss function is used to optimize the weights of ANN. The experimental results indicate that ANNCP can produce promising results under different conditions as compared with other state-of-the-art methods reported in the literature.

A hybrid statistical regression technical for prediction wastewater inflow

One of the appropriate methods for parameter-less prediction is estimation of wastewater flow using simulation parameters by Monte Carlo approach. Appropriate numerical and analytical methods have been developed to simulate wastewater discharge, but none of them can be used reliably before calibration. Therefore, it seems necessary to build a model that can predict the discharge of wastewater more quickly and with general information. Among the new methods used to model the prediction of phenomenon behavior are intelligent data-driven methods. In the present paper, a hybrid multi-objective algorithm called Group Method of Data Handling (GMDH) was used. This method was used to estimate the flow rate discharge for two stations, Zabol and Zahedan. Upon analyzing and learning the hidden relations between the inputs, the GMDH-based neural network logically presented the optimal model and predicted the output variables. Using Fourier and Monte Carlo analysis, flow-type data were constructed for these networks, in turn by which, the flow over the desired interval times was calculated. According to the results of the two stations, among the two models of GMDH and GA + GMDH, the hybrid model of genetic algorithm with GMDH has accounted for the best accuracy. As for the best accuracy, at the Zabol station in October, the statistical parameters obtained for RMSE, d, EF, CI and rp were 5.19, 0.997, 0.991, 0.985 and 0.99, respectively, and as for the Zahedan station in April in terms of statistical parameters it was estimated about 1.11, 0.998, 0.992, 0.999 and 0.99, respectively. At the same time, Taylor’s diagram confirms this accuracy.

Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Studying the relationships between nutrients in pistachio leaves and its yield using hybrid GA-ANN model-based feature selection

Sustainable and reliable management requires special attention to factors affecting crop yield. In the present study, a hybrid model of genetic algorithm and artificial neural network (GA-ANN) was employed to recognize the importance of nutrients in pistachio yield. One hundred seventy-five points in different pistachio orchards of Rafsanjan and Anar regions, Kerman province, the southeast of Iran, were identified and selected for leaf sampling and yield measurement. The concentration of phosphorus (P), potassium (K), iron (Fe), zinc (Zn), copper (Cu), manganese (Mn), calcium (Ca) and magnesium (Mg) was determined. The hybrid GA-ANN model was implemented in MATLAB software, after statistical analysis and multivariate regression modeling. The results showed that the correlation and linear multiple regression analysis could not justify the variations of pistachio yield in relation to leaves' nutrients concentration. The lowest error of the hybrid GA-ANN model was observed by five features including concentrations of K, Mg, Fe, Zn and Cu. Sensitivity analysis of ANN indicated that the highest relative importance for predicting pistachio yield was related to Cu (34.6%), K (28.2%) and Fe (26.1%). The GA-ANN model was able to solve complex and multi-dimensional problems. The accurate and careful interpretation of the results, obtained from this approach can provide a good insight for optimum farm management planning.

Forecasting municipal solid waste generation using artificial intelligence models—a case study in India

Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory–artificial neural network (DWT-ANN), discrete wavelet theory–adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm–artificial neural network (GA-ANN) and genetic algorithm–adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

AN EFFICIENT HYBRID MACHINE LEARNING METHOD FOR TIME SERIES STOCK MARKET FORECASTING

Time series forecasting like stock price predicting is one of the most important complications in the financial area with non-stationary and highly-noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method,the output values are further fed to a developed ANN algorithm to fix the error on exact point. Our analysis suggests that the GA and ANN can increase the accuracy in less iteration.We analyzed the 200-day main index as well as five of the companies listed on the NASDAQ. By applying the proposed method to the Apple stock dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms; we reach to improvement in SSE and timeimprovement to traditional methods. These results show these performances and the speed and the accuracy of our proposed approach.

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R 2), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.

A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs

With decreasing water availability as a result of climate change and human activities, analysis of the influential factors and variation trends of chlorophyll a has become important to prevent reservoir eutrophication and ensure water supply safety. In this paper, a structurally simplified hybrid model of the genetic algorithm (GA) and the support vector machine (SVM) was developed for the prediction of monthly concentration of chlorophyll a in the Miyun Reservoir of northern China over the period from 2000 to 2010. Based on the influence factor analysis, the four most relevant influence factors of chlorophyll a (i.e., total phosphorus, total nitrogen, permanganate index, and reservoir storage) were extracted using the method of feature selection with the GA, which simplified the model structure, making it more practical and efficient for environmental management. The results showed that the developed simplified GA-SVM model could solve nonlinear problems of complex system, and was suitable for the simulation and prediction of chlorophyll a with better performance in accuracy and efficiency in the Miyun Reservoir.

Clavulanic acid production estimation based on color and structural features of streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm

The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively.

A hybrid model of genetic algorithm with local search to discover linguistic data summaries from creep data

A hybrid model of Genetic Algorithm (GA) with local search to discover linguistic summaries and its application into the creep data analysis is proposed in this paper. Two specifics operator and a called Diversity term in the fitness function are introduced by the model to guarantees summaries with high quality and a wide range of information respectively. The experiments show that the hybrid model improves the results compared to those obtained using the classical model of GA. The quality of the summaries was verified by the interpretation of some of them from the theoretical point of view.

Validation of the Revised Stressful Life Event Questionnaire Using a Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Objectives. Stressors have a serious role in precipitating mental and somatic disorders and are an interesting subject for many clinical and community-based studies. Hence, the proper and accurate measurement of them is very important. We revised the stressful life event (SLE) questionnaire by adding weights to the events in order to measure and determine a cut point. Methods. A total of 4569 adults aged between 18 and 85 years completed the SLE questionnaire and the general health questionnaire-12 (GHQ-12). A hybrid model of genetic algorithm (GA) and artificial neural networks (ANNs) was applied to extract the relation between the stressful life events (evaluated by a 6-point Likert scale) and the GHQ score as a response variable. In this model, GA is used in order to set some parameter of ANN for achieving more accurate results. Results. For each stressful life event, the number is defined as weight. Among all stressful life events, death of parents, spouse, or siblings is the most important and impactful stressor in the studied population. Sensitivity of 83% and specificity of 81% were obtained for the cut point 100. Conclusion. The SLE-revised (SLE-R) questionnaire despite simplicity is a high-performance screening tool for investigating the stress level of life events and its management in both community and primary care settings. The SLE-R questionnaire is user-friendly and easy to be self-administered. This questionnaire allows the individuals to be aware of their own health status.

A hybrid model of genetic algorithm and support vector regression for aviation safety prediction

A hybrid model of genetic algorithm and support vector regression for aviation safety prediction