scholarly journals Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 631
Author(s):  
Ivan Contreras ◽  
Remei Calm ◽  
Miguel A. Sainz ◽  
Pau Herrero ◽  
Josep Vehi
Keyword(s):  
Interval Analysis ◽  
Prediction Models ◽  
Fitness Function ◽  
Search Space ◽  
Grammatical Evolution ◽  
Water Current ◽  
Mean Velocity ◽  
Modal Interval ◽  
River Stretch ◽  
Novel Approach

Complex systems are usually affected by various sources of uncertainty, and it is essential to account for mechanisms that ensure the proper management of such disturbances. This paper introduces a novel approach to solve symbolic regression problems, which combines the potential of Grammatical Evolution to obtain solutions by describing the search space with context-free grammars, and the ability of Modal Interval Analysis (MIA) to handle quantified uncertainty. The presented methodology uses an MIA solver to evaluate the fitness function, which represents a novel method to manage uncertainty by means of interval-based prediction models. This paper first introduces the theory that establishes the basis of the proposed methodology, and follows with a description of the system architecture and implementation details. Then, we present an illustrative application example which consists of determining the outer and inner approximations of the mean velocity of the water current of a river stretch. Finally, the interpretation of the obtained results and the limitations of the proposed methodology are discussed.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals A Novel Approach for Operating Speed Continuous Predication Based on Alignment Space Comprehensive Index

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ying Yan ◽  
Gengping Li ◽  
Jinjun Tang ◽  
Zhongyin Guo
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Three Dimensional ◽  
Safety Evaluation ◽  
Driving Safety ◽  
Operating Speed ◽  
Passenger Cars ◽  
Comprehensive Index ◽  
Novel Approach ◽  
Lane Width

Operating speed is a critical indicator for road alignment consistency design and safety evaluation. Although extensive studies have been conducted on operating speed prediction, few models can finish practical continuous prediction at each point along alignment on multilane highways. This study proposes a novel method to estimate the operating speed for multilane highways in China from the aspect of the three-dimensional alignment combination. Operating speed data collected in field experiments on 304 different alignment combination sections are detected by means of Global Positioning System. First, the alignment comprehensive index (ACI) is designed and introduced to describe the function accounting for alignment continuity and driving safety. The variables used in ACI include horizontal curve radius, change rate of curvature, deflection angle of curve, grade, and lane width. Second, the influence range of front and rear alignment on speed is determined on the basis of drivers’ fixation range and dynamical properties of vehicles. Furthermore, a prediction model based on exponential relationships between road alignment and speeds is designed to predict the speed of passenger cars and trucks. Finally, three common criteria are utilized to evaluate the effectiveness of the prediction models. The results indicate that the prediction models outperform the other two operating speed models for their higher prediction accuracy.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

scholarly journals A Robust UWSN Handover Prediction System Using Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5777
Author(s):  
Esraa Eldesouky ◽  
Mahmoud Bekhit ◽  
Ahmed Fathalla ◽  
Ahmad Salah ◽  
Ahmed Ali
Keyword(s):  
Decision Tree ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Performance Metrics ◽  
Prediction Models ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Water Current ◽  
Gradient Boosting ◽  
Marine Data

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

Visualizing Emergency Department Admission through Data Mining

2019 ◽  
Vol 1 (2) ◽  
pp. 19-31
Author(s):  
Kalaivani S ◽  
Shalini Dhiman ◽  
Rajagopal T.K.P.
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Prediction Models ◽  
Personal Data ◽  
Mobility Data ◽  
Novel Approach ◽  
Key Factor ◽  
Ed Overcrowding ◽  
Historic Data ◽  
Short Time

Emergency Department (ED) boarding –the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. Based on these  factors, a  real-time prediction  model is  developed which  is able  to correctly predict  the  admission  result  of  four  out  of  every  five  ED  patients.  The  proposed admission  model  can  be  used  by inpatient  units  to  estimate  the  likelihood  of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using  similar prediction models,  hospitals can evaluate their short-time needs for inpatient care more accurately Emergency Department (ED) boarding – the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. The proposed admission model can be used by inpatient units to estimate the likelihood of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using similar prediction models, hospitals can evaluate their short-time needs for inpatient care more accurately. We use three algorithms to build the predictive models: (1) logistic regression, (2) decision trees, and Analytic tools (accuracy=80.31%, AUC-ROC=0.859) than the decision tree accuracy=80.06%, AUC-ROC=0.824) and the logistic regression model (accuracy=79.94%, AUC-ROC=0.849). Drawing on logistic regression, we identify several factors related to hospital admissions including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. From a different perspective, the research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on this research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and the result also confirmed that context aspects such as demographics and different personal data categories.

Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

2020 ◽  
pp. 173-200
Author(s):  
Logeswaran K. ◽  
Suresh P. ◽  
Savitha S. ◽  
Prasanna Kumar K. R.
Keyword(s):  
Evolutionary Algorithm ◽  
Pattern Mining ◽  
Fitness Function ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Dynamic Selection ◽  
Learning Techniques ◽  
Optimal Function ◽  
High Utility ◽  
Mining Algorithms

In recent years, the data analysts are facing many challenges in high utility itemset (HUI) mining from given transactional database using existing traditional techniques. The challenges in utility mining algorithms are exponentially growing search space and the minimum utility threshold appropriate to the given database. To overcome these challenges, evolutionary algorithm-based techniques can be used to mine the HUI from transactional database. However, testing each of the supporting functions in the optimization problem is very inefficient and it increases the time complexity of the algorithm. To overcome this drawback, reinforcement learning-based approach is proposed for improving the efficiency of the algorithm, and the most appropriate fitness function for evaluation can be selected automatically during execution of an algorithm. Furthermore, during the optimization process when distinct functions are skillful, dynamic selection of current optimal function is done.

scholarly journals A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3765 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Yunfei Xiang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Fitness Function ◽  
Deep Belief Network ◽  
Structure Parameters ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Belief Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

Calibration of Voting-Based Helpfulness Measurement for Online Reviews: An Iterative Bayesian Probability Approach

2020 ◽  
Author(s):  
Xunhua Guo ◽  
Guoqing Chen ◽  
Cong Wang ◽  
Qiang Wei ◽  
Zunqiang Zhang
Keyword(s):  
User Study ◽  
Prediction Models ◽  
Online Reviews ◽  
User Generated Content ◽  
Bayesian Probability ◽  
Social Media Analytics ◽  
Science Literature ◽  
Data Set ◽  
Novel Approach ◽  
Wide Range

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

scholarly journals Sine-Cosine Optimization-Based Bijective Substitution-Boxes Construction Using Enhanced Dynamics of Chaotic Map

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Amer Awad Alzaidi ◽  
Musheer Ahmad ◽  
Hussam S. Ahmed ◽  
Eesa Al Solami
Keyword(s):  
Fitness Function ◽  
Chaotic Map ◽  
Search Space ◽  
Initial Population ◽  
Substitution Boxes ◽  
Novel Method ◽  
Sine Cosine Algorithm ◽  
And Performance ◽  
Optimization Mechanism ◽  
Independence Criterion

This paper proposes a novel method of constructing strong substitution-boxes (S-boxes) of order n (4 ≤ n ≤ 8) based on a recent optimization algorithm known as sine-cosine algorithm (SCA). The paper also proposes a new 1D chaotic map, which owns enhanced dynamics compared to conventional chaotic map, for generating initial population of S-boxes and facilitating the optimization mechanism of SCA. The proposed method applies the SCA with enhanced chaotic map to explore and exploit the search space for obtaining optimized S-boxes on the basis of maximization of nonlinearity as fitness function. The S-box construction involves three phases such as initialization of population, optimization, and adjustment. The simulation and performance analyses are done using standard measures of nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, and autocorrelation function. The obtained experimental results are compared with some immediate optimization-based and other S-boxes to show the strength of proposed method for constructing bijective S-boxes of salient cryptographic features.

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