Genetic Fuzzy Research Articles

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

Performance analysis of vehicle magnetorheological semi-active air suspension based on S-QFSMC control

The performance of the suspension is a crucial criterion for evaluating both vehicle handling and passenger comfort. To enhance suspension performance, this study proposes the design of a Quantum Genetic Fuzzy Sliding Mode Controller (S-QFSMC) based on the Smith predictor estimator, building upon the foundation of the vehicle magneto-rheological semi-active air suspension. According to the physical model of the vehicle suspension, a mechanical model of a quarter-vehicle magneto-rheological semi-active air suspension with time delay is established. On this basis, a conventional sliding mode controller is designed, and quantum genetic algorithm and fuzzy control principles are employed to optimize the chattering issue associated with sliding mode control. The Smith predictor estimator is utilized to effectively compensate for the time delay in the suspension system. Subsequently, a simulation analysis of the vehicle suspension performance is conducted. The results indicate that, compared to passive suspension control, both the QFSMC controller and the S-QFSMC controller improve the suspension performance, with the S-QFSMC controller exhibiting superior comprehensive improvement. This validates the effectiveness of the designed controllers.

Genetic Fuzzy Learning of local wind conditions

The wind climate measured in a point could be described as the regional wind climate of the zone forced by local effects derived from topography, roughness and obstacles in the surrounding area. This paper presents a method that allows to find fuzzy relations between the local wind climate in a location and the physical geography of the area. The fuzzy sets used in this work will connect relief elements with a probability relationed with the chances of flow over the concerned element. Thus, evaluating the heights and slopes of the terrain around the selected point, each fuzzy system will generate a possible local wind climate, which will be compared with the real one. In order to obtain the best agreement, a population of these fuzzy systems will be improved using Genetic Algorithms.

Enhancing Predictive Accuracy of Insurance Stock Market in Jordan using Hyprid GFS.Thrift Model: A Genetic Fuzzy System-based Fintech Approach

This study focuses on improving the predicting accuracy of the daily ASE's weighted price index of the insurance sector (ICI) using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6). Using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions—Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6)—this study aims to increase the daily ASE's weighted price index of the insurance sector's (ICI) prediction accuracy. The model utilizes a genetic fuzzy system based on Thrift's methodology (GFS.Thrift). The Amman Stock Exchange (ASE) supplied a dataset with 4,478 observations for the purpose of the study. The dataset represented daily data from January 2, 2006, to March 24, 2024. The adaptive GFS.THRIFT model was trained with 90% of the dataset, while the remaining 10% was used to test its prediction performance. Multiple egressions and multicollinearity tests were used to select input variables such as standardized foreign direct investment (FDI), standardized value traded (VT) and consumer price index (CPI). Insights from this study indicate that all input variables are positively related to the output variable. Secondly, the proposed model (MODWT-Haar-GFS. Thrift) significantly outperforms other existing models including the GFS. Thrift model.

GENETIC FUZZY LOGIC ALGORITHM AS INTELLIGENT AGENTS FOR SWARM INTELLIGENCE APPLICATION

This research introduces a novel approach to harnessing the power of Genetic Fuzzy Logic Algorithms (GFLAs) in the context of Swarm Intelligence applications. Swarm Intelligence relies on decentralized, self-organized systems, where individual agents collaborate to achieve complex tasks. However, existing methods often face challenges in adapting to dynamic environments and optimizing system performance. To address this, our study proposes the integration of GFLAs as intelligent agents within Swarm Intelligence frameworks. GFLAs leverage genetic algorithms and fuzzy logic to evolve and adapt their rules autonomously, enhancing the system adaptability and efficiency. The research addresses the gap in current literature by investigating the potential of GFLAs as intelligent agents in Swarm Intelligence, emphasizing their ability to learn and optimize behaviors in real-time. Through rigorous experimentation, we demonstrate the effectiveness of the proposed method in improving swarm performance across diverse scenarios.

Knowledge in Medical Prescriptions with the Help of Genetic Fuzzy Hybrid Model

Following the progress made in the construction of high-speed processors, the limiting effect of the way data is entered into the computer on the speed of information transfer has become more apparent. By using processing devices, in addition to achieving higher speed in the data retrieval stage, it is also possible to use their pre-processing capabilities and change the data format. Considering the importance of the topic and the work that has been done in this field, the need to discover the knowledge of the existing features with the help of choosing the appropriate feature for the classification of texts is well felt. In this article, genetic algorithm and fuzzy logic have been used to present a method for classifying texts in insurance booklets. This system is based on several stages. These steps include the learning phase that examines a set of educational texts to extract the characteristics of the categories to be the characteristics of each category; the test phase of the system is used to classify uncategorized texts. The proposed method's accuracy has been assessed using a collection of patient notebooks, and the outcomes indicate an accuracy of around 98% for the classification.

Guide vane profile optimization of pump-turbine for grid connection performance improvement

Based on the vigorous development of global renewable energy, pumped storage hydro (PSH) has been widely used. As PSH needs to undergo multiple modes conversion, its grid connection performance and operation stability are difficult to be guaranteed. In this study, the method of combining numerical simulation with experiment is adopted, aiming at the situation that the guide vane opening is 5° of a pump-turbine unit, the Center-Radius guide vane profile parameterization method combined with B-spline curve and Genetic Fuzzy Algorithm are proposed, the strength of guide vane blade is taken as constraint. The guide vane profile is optimized, the grid connection characteristics and operation stability between initial and optimized units are analyzed. The results show that based on the optimization strategy proposed in this study, the optimization of guide vane shape can effectively improve the operation stability and grid connection performance, the study also provides an optimization idea for the further development of PSH.

Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team.

E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering

E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering

Urban growth modeling based on cellular automata with transition rules optimized using genetic fuzzy systems

AbstractThis article presents a new methodology for urban growth modeling by integrating cellular automata (CA) and genetic fuzzy algorithms. The urban growth phenomenon is a spatio‐temporal and continuous process that can be modeled by employing fuzzy logic as a framework for handling vagueness. Dependence upon an expert for assigning membership functions and rule weights has made the fuzzy logic approach rather subjective. Our proposed methodology overcomes this shortcoming by applying genetic algorithms to tune and optimize the fuzzy logic system and to make it expert independent. The optimization process encompasses a fuzzy membership function and weights for fuzzy rules. This model uses linguistic variables for defining CA transition rules and applies them to represent the non‐deterministic nature of urban growth. The proposed model is applied to simulate urban growth in the Tehran Metropolitan Area in Iran across time steps of 1988, 1999, and 2010 developed using Landsat TM and ETM+ images and a Digital Elevation Model. The first data pairs were employed for calibration of the model parameters, and the remainder of the data was used for validation of the model across time. The model was evaluated using a relative operating characteristic of 0.88 and a figure of merit statistic of 0.31, which quantifies the model goodness of fit.

Seismic control of the structures with active tuned mass damper and variable fractional order fuzzy proportional–integral–derivative controller

The structural control of the building is a critical part of mitigating the catastrophic consequences of the earthquakes. The active control of structures has gained popularity in the construction industry because of its ability to adapt to different ground motion characteristics. The active tuned mass damper (ATMD) is one of the most versatile devices used for active structural control. This paper uses the ATMD and variable fractional order fuzzy PID (VFOFPID) controller for improving the performance of building structures subjected to horizontal ground motions. Having obtained the response of an 11-story shear building with this controller, the optimized control forces are determined through the pattern search algorithm and the performance indices of the system subjected to 100 different ground motions are evaluated. The performance indices are compared with those of uncontrolled as well as genetic fuzzy and fuzzy PID controlled structure. The results indicate that the VFOFPID controller proposed here can effectively reduce the drift and the absolute acceleration of the structure.

Hybrid Genetic Fuzzy System for Modeling Consumer Behavior

Understanding consumer behavior is beneficial to a business in various aspects such as prediction of manufacturing quantity, new product launch, and aids in lock-in customers and lock-out competitors. The task is highly complex and traditional models do not help in absence of generalized decision making logic. Further such domains handle large amount of data in unstructured format. This article presents an intelligent system for modeling consumer behavior via a hybrid genetic fuzzy system from large source of data. The paper justifies and presents a literature survey with common observations. A four phase generic architecture of genetic fuzzy system presented for the modeling of consumer behavior. Detailed discussion on the architecture is also provided with an experiment. Technical details, fuzzy membership functions used in experiment, encoding strategy, genetic operators, and evaluation of rules using fitness function are also discussed in detail along with results. At end, applications of the research work in other domains are enlisted with possible future enhancements.

To solve the problems of combat mission predictions based on multi-instance genetic fuzzy systems

To solve the problems of combat mission predictions based on multi-instance genetic fuzzy systems

Performance of Structure Equipped AP‐TMD Compared with MTMD, ATMD, and PTMD against Earthquake Using Genetic Fuzzy Algorithm

During the past years, different control devices have been introduced and used to reduce the response of structures. This article presents the performance of active‐passive tuned mass dampers (AP‐TMDs) in the reduction of structural responses and a comparison between the uses of different controllers, including tuned mass damper (TMD), active tuned mass damper (ATMD), and multituned mass damper (MTMD). Analyzing and modeling the structure under four near‐ and far‐field earthquakes are performed in MATLAB and SIMULINK. Finally, the responses of controlled and uncontrolled structures equipped with these controllers are investigated. Fuzzy and genetic‐based fuzzy logic controllers are used to determine the control force of ATMD and AP‐TMD, respectively. In order to compare the performance of the dampers, the mass ratio of TMDs is fixed in all cases and is taken to be 5%. Damping of the structure is considered equal to 5%. The frequency and damping ratio of TMDs with maximum and RMS displacement optimization criteria are obtained. The mass ratio of TMDs in controlled structures with MTMD and AP‐TMD is calculated by numerical analysis. It can be inferred that using control force affects response reduction in both ATMD and AP‐TMD controllers significantly. In addition, using AP‐TMD can bring the merits of both passive and active systems by providing active control and reducing power requirements for control forces. All controllers are recommended for structural control although AP‐TMD reduced maximum and RMS displacement by about 50% in the best case, which has better performance than others. On the other hand, ATMD decreased RMS acceleration by 37.5% on average in four earthquakes compared to the uncontrolled structure.

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.

Implementation of Genetic Algorithm and Adaptive Neuro Fuzzy Inference System in Predicting Survival of Patients with Heart Failure

Purpose: Heart failure is a disease that is still a global threat and plays a major role as the number one cause of death worldwide. Therefore, accurate predictions are needed to determine the survival of heart failure patients. One technique that can be used to predict a decision is classification. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an algorithm that can be used in the classification process in making predictions. Genetic Algorithms can help improve the performance of classification algorithms through the feature selection process. Methods/Study design/approach: In this study, predictions or diagnoses were made on the survival of heart failure patients based on the heart failure clinical record dataset obtained from the UCI Machine Learning Repository. The data used is 299 data with 12 attributes and 1 class. The result of this research is the comparison of the accuracy of the ANFIS algorithm before and after using the Genetic Algorithm. Result/Findings: The ANFIS algorithm produces the highest accuracy of 94.444%. While the ANFIS algorithm after attribute selection using the Genetic Algorithm produces the highest accuracy of 96.667%. This shows that the Genetic Algorithm is able to improve the performance of the ANFIS classification algorithm through the attribute selection process.

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

The Voltage Stabilizing Control Strategy of Off-Grid Microgrid Cluster Bus Based on Adaptive Genetic Fuzzy Double Closed-Loop Control

In the off-grid microgrid cluster, the energy storage device is mainly charged and discharged to maintain the stability of the bus voltage and the system power balance. Generally, the voltage and current double closed-loop control and fuzzy control are adopted for the energy storage converter. The traditional double closed-loop control parameters and the scale factor and quantization factor in fuzzy control cannot be adjusted in real time during system operation, resulting in slower dynamic response and weak anti-interference ability of the system. In response to the above problems, this paper proposes an adaptive genetic fuzzy double closed-loop control, which can adjust the PI control parameters in real time by adjusting the quantization factor and the scale factor to optimize the control effect. The simulation platform is built in MATLAB/Simulink and the simulation results show that the adaptive genetic fuzzy double closed-loop control combines the advantages of fuzzy and PI control. Under different working conditions, the system has not only a fast dynamic response, small overshoot, and strong anti-interference ability but also good robustness.

Genetic fuzzy optimized approximate multiplier design based non-linear anisotropic diffusion image denoising in VLSI

Multiplier is a building block used in processor, embedded, Very Large-Scale Integration (VLSI) applications and integrated circuits. There are three main parameters of VLSI design lies in speed, area and power. Approximate computing is a developing one in digital design used to increase the performance. Many conventional methods are introduced for efficient approximate multiplier design. A new approximation multiplier design approach was presented with partial products of multiplier. A new approximate compressor was designed with higher power or speed for target precision. However, the designed approach failed to minimize the design complexity. In order to reduce power consumption and design complexity during the noise removal process, Genetic Fuzzy Optimized Approximate Multiplier based Non-Linear Anisotropic Diffusion Image Denoising (GFOAM-NLADID) Method is introduced. The key objective of the GFOAM-NLADID Method is to perform efficient approximate design using genetic fuzzy concepts for reducing the noise artifacts from input images. GFOAM-NLADID method used genetic fuzzy optimization algorithm to find optimal component (i.e., full adder type, compressor, and tree) for constructing the approximate multiplier design. The optimal component is selected after calculating the fuzzy fitness function. When the fitness function satisfies the fuzzy rule, the component is chosen for efficient design. When the criteria are not satisfied by the particular component, selection, crossover and mutation process is performed to choose the global optimal solution. By choosing the optimal component in GFOAM-NLADID Method, power consumption and design complexity level gets reduced. After designing the approximate multiplier, Non-Linear Anisotropic Diffusion Image Denoising process is carried out in GFOAM-NLADID Method to remove the noisy artifacts and to improve the image quality performance. This in turn helps to improve the peak signal-to-noise ratio performance by GFOAM-NLADID method. Experimental evaluation of GFOAM-NLADID Method is carried out with performance metrics such as power consumption, filtering time and peak signal-to-noise ratio compared to the state-of-the-art works.

A genetic fuzzy contention window optimization approach for IEEE 802.11 WLANs

The role of IEEE 802.11 wireless local area networks (WLANs) become vital due to its low cost deployment and the aspiration to improve its performance has become the need of the day. Among existing challenges the most prominent are success ratio, packet loss ratio, collision rate, fairness index, and energy consumption, advances in wireless technologies stress to surmount these challenges. To attain these, performance enhancement, genetic fuzzy-contention window optimization (GF-CWO) approach, is proposed which combined the fuzzy logic controller (FLC) and genetic algorithm (GA), through this way GA optimally tuned the FLC. For this purpose three alogrithms are proposed, namely Algorithm-1: GF-CWO for WLANs, Algorithm-2: GA for GF-CWO, and Algorithm-3: FLC for GF-CWO. Proposed GF-CWO approach is tested for binary exponential back-off (BEB), selected being a de facto standard algorithm, and for Channel Status based Sliding Contention Window (CS-SCW) algorithm, selected being a fuzzy logic based algorithm, implemented in MATLAB. Recorded the resultant values for 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nodes in different files for BEB, CS-SCW, and GF-CWO, respectively. Later on these files were used to evaluate success ratio, packet loss ratio, collision rate, fairness index and energy consumption and also generated the results graphically. The results generated through simulated test confirmed that the GF-CWO has effectively enhanced the performance.