Genetic Fuzzy Research Articles

Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding vehicles, thus making this a decentralized system. The GFS module in each CoAV makes recommendations on the acceleration and lane-change decisions. The CoAVs are trained on different ADS behavioral parameters, such as aggressiveness of lane change, different CoAV market penetration rates (MPRs) and traffic congestion levels. The results show the effectiveness of increasing the MPR. It is noticed that the CoAVs trained with different ADS behavioral parameters can generate completely different cooperative maneuvers. The results also show that the trained CoAVs can cooperate even with human-driven vehicles to improve the overall traffic performance.

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task.

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

Predicting Post-Concussion Symptom Recovery in Adolescents Using a Novel Artificial Intelligence.

This pilot study explores the possibility of predicting post-concussion symptom recovery at one week post-injury using only objective diffusion tensor imaging (DTI) data inputs to a novel artificial intelligence (AI) system composed of Genetic Fuzzy Trees (GFT). Forty-three adolescents age 11 to 16 years with either mild traumatic brain injury or traumatic orthopedic injury were enrolled on presentation to the emergency department. Participants received a DTI scan three days post-injury, and their symptoms were assessed by the Post-Concussion Symptom Scale (PCSS) at 6 h and one week post-injury. The GFT system was trained using one-week total PCSS scores, 48 volumetric magnetic resonance imaging inputs, and 192 DTI inputs per participant over 225 training runs. Each training run contained a randomly selected 80% of the total sample followed by a 20% validation run. Over a different randomly selected sample distribution, GFT was also compared with six common classification methods. The cascading GFT structure controlled an effectively infinite solution space that classified participants as recovered or not recovered significantly better than chance. It demonstrated 100% and 62% classification accuracy in training and validation, respectively, better than any of the six comparison methods. Recovery sensitivity and specificity were 59% and 65% in the GFT validation set, respectively. These results provide initial evidence for the effectiveness of a GFT system to make clinical predictions of trauma symptom recovery using objective brain measures. Although clinical and research applications will necessitate additional optimization of the system, these results highlight the future promise of AI in acute care.

Fuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisal

Screening is a key stage in environmental impact assessment (EIA), but the most common approach based on policy delineation are inherently arbitrary. On the other hand, a case-by-case approach can be complex, slow, and costly. This paper introduces a computational intelligence based on hybrid fuzzy inference system (h-FIS), combining data-driven and expert knowledge, in order to assess its capability of supporting a case-by-case screening in project appraisal. For empirical research, a dataset with appraisal variables of projects highway was made available by a Brazilian environmental protection agency (EPA). Firstly, using this dataset, multivariate analyses were performed to find criteria (xi) capable of indicating statistically significant differences among projects, previously screened by EPA experts into three types (simplified, preliminary, and comprehensive) of environmental impact study (EIS). Then, h-FIS was built through machine learning, using the FRBCS·W algorithm, with xi as input predictors and the type of EIS as the output target. The performances of alternative approaches were compared using cross-validation accuracy tests and the kappa index, with a significance level of 0.05. As a result, the h-FIS achieved accuracy of 92.6% and a kappa index of 0.88, which represented almost perfect agreement between the screening decision provided by the h-FIS and the one performed by the EPA experts. In conclusion, the fuzzy-based computational intelligence was capable of dealing with the complexity involved in screening decision. Therefore h-FIS be considered a promising complementary tool for a case-by-case project appraisal in EIA. For further advances, future research should assess other algorithms, such as genetic fuzzy systems, in order to strengthen the proposed system and make it generally applicable in other projects subject to EIA.

Predicting Protracted Concussion Recovery To Inform Proactive Care: A Genetic Fuzzy Machine Learning Approach

Poor prognostic accuracy of sport-related concussion (SRC) recovery times has limited proactive clinical care. Currently, clinicians consolidate a battery of assessments and combine their own practical knowledge to develop prognosis and treatment plans. Machine learning may provide a useful method to augment clinicians’ prognostic decision making: a critical first step to enhance proactive care. PURPOSE: Determine utility of a novel genetic fuzzy system (GFS) machine learning approach, FuzzyBolt, to predict protracted recovery after SRC. METHOD: Data from 76 pediatric patients (age 14.44 ± 2.54 years; 28 F) were obtained from 186 combined clinic visits following initial SRC. Recovery time was indexed from the physician-recorded full clearance to return to play date in the medical record and then classified as less than or equal to 28 days (N=88) vs. greater than 28 days (N=98)—the consensus pediatric threshold for persistent symptoms. A GFS model classified protracted recovery on patients that were less than 28 days in recovery. GFS uses fuzzification, rule-inference and defuzzification to make decisions, and FuzzyBolt provides an efficient method of optimizing model parameters via genetic algorithms. The model used 36 inputs, including ordinal and binary variables related to patient demographics, standardized Post-Concussion Symptom Inventory responses, and self-reported responses from a clinic-based Head Injury Questionnaire. RESULTS: Data were split, via stratified sampling, into a training set (80%; 61 athletes with 151 visits) and a hold-out validation set (20%; 15 athletes with 35 visits). Each patient visit was considered a unique case, with visits from the same patient never part of both the training and validation sets to reduce the risk of over-fitting and inflation of non-generalizable prediction accuracy. The FuzzyBolt model correctly predicted 12 of 16 protracted (and 16 of 19 typical) recovery cases, for an overall classification accuracy of 80%. CONCLUSION: This is the highest prediction accuracy, to date, for any published prognostic model of concussion recovery. It is a first step toward promoting early allocation of resources for patients at high-risk for protracted recovery, and demonstrates a novel technique to empower a data-driven solution to improve outcomes in these athletes.

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

A Genetic Fuzzy Model for Investigating Security and Trust in E-Commerce with Genetic Algorithm

A Genetic Fuzzy Model for Investigating Security and Trust in E-Commerce with Genetic Algorithm

A Unified Framework of Sentimental Analysis for Online Product Reviews Using Genetic Fuzzy Clustering with Classification

A Unified Framework of Sentimental Analysis for Online Product Reviews Using Genetic Fuzzy Clustering with Classification

Driving Behavior Clustering for Hazardous Material Transportation Based on Genetic Fuzzy C-Means Algorithm

In recent years, many accidents of hazardous material transportation have been caused by dangerous driving behavior such as excessive speed or rapid speed change. To solve the problem, in this paper a series of indicators for driving behavior evaluation are selected for quantitative analysis based on the massive data of the operating vehicle networked control system. After that, three main indicators are introduced in detail, that is, the acceleration & deceleration behavior indicator, the over speed behavior indicator and the operation stability indicator. Then by using the GA-FCM clustering method, 40 drivers of hazardous material transportation are classified according to their behavior parameters. The results clearly show that the driving speed of the drivers with high-risk driving behavior, and most of the drivers with poor driving stability, are also at bad over speed level. Therefore, this paper creatively applies the method of combining factor analysis and GA-FCM clustering to the field of driving behavior of hazardous material transportation. It will facilitate enterprises and management departments to focus on controlling high-risk drivers, thereby reducing accidents.

Adaptive Genetic Fuzzy Decisive Technique Based Moving Target Identification using Multichannel SAR Set-up

In the ongoing synthetic aperture radar (SAR) methodology, precise and efficient identification of moving targets is a prominent task. Fractional FT (FrFT)accumulates the energy of the required chirp signal in order to separate it as noise from the chirp.The proposed SAR Moving Target Identification (MTI) process is based on FrFT being combined with the definitive adaptive genetic or neurofuzzy method. The correlation between the transmitted signal and the received signal's FrFT is determined, optimizing the appropriate signal energy and applying it to the decisive adaptive genetic fuzzy unit, which identifies the object location using the fuzzy linguistic rules adaptively.The simulation is conducted by changing the number of targets and number of iterations and the evaluation is performed based on parameters such as missed target rate, detection time and Mean Square Error (MSE), showing that the proposed Adaptive Genetic Fuzzy decisiveMTIsystem located the object with a minimum missed target rate of 0.12 in 5.02s and MSE of 23377.4

Design and Implementation of Genetic Fuzzy Controller for Split Air-Conditioner Control Based on Fanger’s PMV Index

This paper reports the design and implementation of genetically optimized fuzzy logic controller (GAFLC) for split air-conditioner based on the principle of Fanger’s Predicted Mean Vote (PMV) index. The proposed control strategy is aimed at improving the indoor thermal environment (ITE) at houses, offices, libraries, hotels, etc. because it plays a vital role in determining the health, physical and mental productivity of the occupants. The GAFLC has been implemented in MATLAB Simulink for computer simulation and also on hardware platform using the commercially available 8-bit ATmega-328 microcontroller through embedded C-coding for real practice. One part of the designed control algorithm examines the values of activity level, clothing insulation, air velocity, and relative humidity and decides the comfort temperature value to be set such that the PMV and PPD indices get satisfied. The other part generates a control signal to the air-conditioner compressor to maintain that temperature. From the simulation results it is seen that the generated comfort temperature values are in the range of 24.4∘– 26.55∘C for various combinations of environmental and personal parameters, which are well above the general temperature set value of 20∘C. This indicates the scope for reducing energy consumption to a greater extent. Also the PMV index lies in the range of [Formula: see text]0.23 to +[Formula: see text]0.36 with untuned fuzzy inference system (FIS), and in the range of [Formula: see text]0.32 to +[Formula: see text]0.14 with genetic algorithm (GA)-tuned FIS, which are acceptable comfort levels that human physiology can endure with more satisfaction. The experimental results show that GAFLC has generated a comfort temperature value for specified input parameters and also maintained the room temperature at that value to keep the thermal ambience more satisfactorily.

Intelligent optimization algorithms for the problem of mining numerical association rules

There are many effective approaches that have been proposed for association rules mining (ARM) on binary or discrete-valued data. However, in many real-world applications, the data usually consist of numerical values and the standard algorithms cannot work or give promising results on these datasets. In numerical ARM (NARM), it is a difficult problem to determine which attributes will be included in the rules to be discovered and which ones will be on the left of the rule and which ones on the right. It is also difficult to automatically adjusting of most relevant ranges for numerical attributes. Directly discovering the rules without generating the frequent itemsets as used in the literature as the first step of ARM accelerates the whole process without determining the metrics needed for this step. In classical ARM algorithms, generally one or two metrics are considered. However, mined rules are needed to be comprehensible, surprising, interesting, accurate, confidential, and etc. in many real-world applications. Adjusting all of these processes without the need for the metrics to be pre-determined for each dataset seems another problem. For these purposes, evolutionary intelligent optimization algorithms seem potential solution method for this complex problem. In this paper, the performance analysis of seven evolutionary algorithms and fuzzy evolutionary algorithms; namely Alatasetal, Alcalaetal, EARMGA, GAR, GENAR, Genetic Fuzzy Apriori, and Genetic Fuzzy AprioriDC for NARM problem has been performed within eleven real datasets for the first time. The obtained results have also been compared with the classical Apriori algorithm to show the efficiencies of the intelligent algorithms on NARM problem. Performances of eight algorithms in terms of support, confidence, number of mined rules, number of covered records, and time metrics have been comparatively performed with eleven real-world datasets. One of the best-mined rules obtained by each algorithm has been given and analyzed with respect to confidence, support, and lift metrics.

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

Deep Learning and Fuzzy Rule-Based Hybrid Fusion Model for Data Classification

Data mining is the promising field that attracted the industries to manage huge volumes of data. The most effective and challenging techniques of data mining is data classification. The main intension of this research is to design and develop a data classification strategy based on hybrid fusion model using the deep learning approach, Adaptive Lion Fuzzy System (ALFS), and Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS). The hybrid model consists of three phases: In the first phase, the data is classified using ALFS and the rule base of the fuzzy system is updated by optimally generating the rules using adaptive lion optimization (ALA) from the training data. The second step is the fuzzification process, which converts the scalar values in the training data into fuzzy values with the help of membership function, which is based on Adaptive Genetic Fuzzy System (AGFS). Finally, the classified score of data instances is determined using defuzzification process, which converts the linguistic variable into fuzzy score. In the second phase, the data is classified using Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS), which is used for selecting the optimal fuzzy rules. In the third phase, the data is classified using deep learning networks. The outputs from three phases are fused together using the hybrid fusion model for which the weighed fusion is employed. The performance of the system is validated using three different datasets that are available in UCI machine learning repository. The proposed hybrid model outperforms the existing methods with sensitivity of 0.99, specificity of 0.9350, and accuracy of 0.9411, respectively.

Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

SummaryThis paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.

Comparison Between Genetic Fuzzy Methodology and Q-Learning for Collaborative Control Design

A comparison between two machine learning approaches viz., Genetic Fuzzy Methodology and Q-learning, is presented in this paper. The approaches are used to model controllers for a set of collaborative robots that need to work together to bring an object to a target position. The robots are fixed and are attached to the object through elastic cables. A major constraint considered in this problem is that the robots cannot communicate with each other. This means that at any instant, each robot has no motion or control information of the other robots and it can only pull or release its cable based only on the motion states of the object. This decentralized control problem provides a good example to test the capabilities and restrictions of these two machine learning approaches. The system is first trained using a set of training scenarios and then applied to an extensive test set to check the generalization achieved by each method.

Feature Selection and Evolutionary Rule Learning for Big Data in Smart Building Energy Management

Since buildings are one of the largest sources of energy consumption in most cities of the world, energy management is one of the major concerns in their design. To ameliorate this problem, buildings are becoming smarter by the incorporation of intelligent supervision and control systems. Data captured by the sensors can be interpreted and processed by rule-based computation methods of biological inspiration (such as genetic fuzzy systems, GFS) for predicting the future behavior of the building in a knowledge-based interpretable human-like manner. GFS are computational models inspired in human cognition which use evolutionary computation (inspired in the natural evolution) to automatically learn fuzzy rules which contain explicit imprecise knowledge about a system or process. This knowledge, represented using fuzzy rules that involve fuzzy linguistic variables and values, is used to perform approximate reasoning on the input values for obtaining inferred values for the output variables. In energy management of buildings, these rules allow a smart control of the system actuators to reduce the building average energy consumption. However, the large amount of data produced on a per second basis complicates the generation of accurate and interpretable models by means of traditional methods. In this paper, we present an evolutionary computation-based approach, namely a genetic fuzzy system, to build scalable and interpretable knowledge bases for predicting energy consumption in smart buildings. For accomplishing this task, we propose a cognitive computation system for multi-step prediction based on S-FRULER, a state-of-the-art scalable distributed GFS, coupled with a feature subset selection method to automatically select the most relevant features for different time steps. S-FRULER is able to learn a fuzzy rule-based system made up of Takagi-Sugeno-Kang (TSK) rules that are able to predict the output values using both linguistic imprecise knowledge (represented by fuzzy sets) and fuzzy inference. Experiments with real data on two different problems related with the energy management revealed an average improvement of 6% on accuracy with respect to S-FRULER without feature selection, and with knowledge bases with a lower number of variables.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

Genetic Fuzzy System for Financial Management

Abstract This paper discusses genetic fuzzy systems – hybrid systems of artificial intelligence combining the potential of fuzzy sets for modeling approximate reasoning with the abilities of genetic algorithms for finding optimal solutions. The use of genetic algorithms for optimizing the parameters of a fuzzy system is demonstrated on GFSSAM.