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There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research. [ABSTRACT FROM AUTHOR]

Background: Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective: The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods: A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results: The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion: Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy. [ABSTRACT FROM AUTHOR]

Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then such data need first to be preprocessed and vectorized (converted to numeric). Second, to overcome the curse of dimensionality caused by the vectorisation process, a dimension reduction is applied. A two-stage variable selection and feature extraction is used to reduce the high dimensionality of a feature space. We are first interested in studying the potential of using an unsupervised variable selection technique, the genetic algorithm, to identify the variables that best demonstrate discrimination in the separation and compactness of groups of textual data. The genetic algorithm uses a combination of the K-means or Gaussian Mixture Model clustering and validity indices as a fitness function for optimization. Such a function improves both compactness and class separation. The second work looks into the feasibility of applying a feature extraction technique. The adopted methodology is a Deep learning algorithm based on variational autoencoder (VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for cluster identification. The last objective of this paper is to propose a new methodology based on the combination between variational autoencoder (VAE) for the latent space disentanglement, and genetic algorithm (GA) to find, in an unsupervised way, the latent variables allowing the best discrimination of clusters of failure analysis data. This methodology is called VAE-GA. Experiments on textual datasets of failure analysis demonstrate the effectiveness of the VAE-GA proposed method which allows better discrimination of textual classes compared to the use of GA or VAE separately or the combination of PCA with GA (PCA-GA) or a simple Auto-encoders with GA (AE-GA). [ABSTRACT FROM AUTHOR]

The authors review a coreset discovery problem and three main methods to solve it: geometric coreset estimation, coreset discovery using the genetic algorithm, and coreset discovery using neural networks. They analyze each of these methods and find the cases where they show the best results. The paper focuses on neural network-based approaches and their ability to solve the coreset discovery problem. The authors perform a comparative analysis of several neural network-based approaches, describe their pros and cons, and determine the further steps in solving the coreset discovery problem. [ABSTRACT FROM AUTHOR]

Music expresses thoughts and emotions in artistic form and is made up of different components including harmony, rhythm, and melody. Several musical elements are tied together during the songwriting process in order to generate melodies that are harmonic. A music melody is the essential element of any music to generate strong feelings from listeners and capture their interest. In the process of music appreciation, melody controls the emotional changes of music. It is an efficiently perceived part and the tone of a song. In recent years, Sichuan unvoiced music has developed rapidly and attracted much attention. This paper selects Sichuan unvoiced music as the main research theme and constructs a melody generation algorithm by utilizing the state-of-the-art techniques of deep learning (DL) and evolutionary algorithms (EAs) such as recurrent neural network-long short-term memory (RNN-LSTM) and genetic algorithm (GA). Firstly, this paper briefly describes the concept of DL algorithms, the deep generation model, and sequence to sequence model, as they constitute the technological foundation for this research. Secondly, this paper proposes a melody generation algorithm that utilizes RNN-LSTM for melody generation and GA for melody optimization. More specifically, the melody is generated by preprocessing data, creating, and training the RNN-LSTM model. A GA was used to determine the melodic fitness function for eight songs as the fitness function directly affects the selected termination condition. The fitness function can be thought of as either a person or an evolutionary rule. Finally, the average score of these songs, before and after evolution, is calculated, which demonstrates that the analysis and rotation creation methods are more precise and that the song's average melody score is higher. The method proposed in this study has been thoroughly compared to the existing approaches proposed in earlier studies, and it was found that the approach we propose is more effective in terms of accuracy and the average melody score. [ABSTRACT FROM AUTHOR]

Calculus equation is an important tool for mathematical research and plays an important role in most natural science research. Since the beginning of the eighteenth century, people have gradually used differential and integral equations to solve physical problems. In general, several different aspects of differential equations in the field of mathematics are concerned and studied by most scholars. However, this paper studies and establishes the optimal model for numerical solution of differential equations through deep learning and genetic algorithm. In this paper, the solution of ordinary differential equations is solved through the use of polynomial function space, while the linear combination of simple function x and its product nx can obtain multinomial function space. The space function form of polynomial is very simple, and the operation ability is very strong. Almost all functions can be approximated, and the function space can be transformed by a simple function. Through data simulation test results, it can be found that the oscillation of neural network output is stronger and stronger with the increase in depth, that is to say, the deeper depth endows the neural network with stronger oscillation properties, so for the oscillation function, the depth neural network fitting effect is better than the shallow neural network. Therefore, by combining deep learning and genetic algorithm, this paper studies and establishes the optimal model for numerical solution of differential equations, and finds that the deep neural network can largely complete data simulation. [ABSTRACT FROM AUTHOR]

Advancements in technology have led to a significant growth of agricultural data. The potential for forecasting crop yield depends on parameters such as climate, soil characteristics, and water factors has emerged as significant research area. Combining a comprehensive dataset from diverse sources like climate, soil and water parameters for precise time series forecasting in agricultural yield prediction is a significant challenge. Therefore, there is a need to integrate this data with agricultural processes to enhance crop production. In this paper, propose Genetic Algorithm based Autoregressive Integrated Moving Average (ARIMA) and Reinforced Random Forest Algorithm forecasting models for enhancing crop yield prediction. The genetic algorithm emulates the evolutionary process, simulating the selection of the strongest individuals over weaker ones through mechanisms such as selection, crossover, and mutation. The new approach incorporates reinforcement learning during the selection of attributes for tree construction by optimizing sample usage. By assessing variable significance, it identifies the most significant variables for node splitting improving model development and maximizing training data utilization. In addition to internal cross-validation, the method minimizes parameter tuning, mitigates overfitting, accelerates computations, and enhances transparency. The proposed model Genetic Algorithm based ARIMA achieves better results for enhancing crop yield forecasting on evaluation metrics, i.e., MAE (0.80%), RMSE (3.75%), RRSE (1.21%), RAE (0.82%), MSE (0.07%), R2 (0.54%) and accuracy (80.00%). For Reinforced Random Forest Algorithm model, i.e. MAE (1.28%), RMSE (2.48%), RRSE (0.54%), RAE (0.91%), MSE (0.55%), R2 (0.98%) and accuracy (78.00%). The analysis reveals that the genetic algorithm based ARIMA model outperforms the reinforced random forest algorithm in terms of efficiency and accuracy. Both proposed models better perform than machine learning classical models excelling not only in enhanced crop yield forecasting but also in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent processing of physical education resources, this paper combines genetic algorithms and unsupervised learning methods to study the processing of physical education videos and images, which approximately maintains the neighbor structure relationship of the original sample data and reduces the loss of sample local information. Moreover, this paper constructs a graph model structure between the comprehensive compressed data and the input data. In order to reduce the complexity of the solution method of the graph model and reduce the impact of the method of decomposing the spectrogram into the characteristic function on the hash algorithm, this paper designs an intelligent physical education resource processing method based on genetic algorithms and unsupervised learning methods and constructs system functional modules. Finally, this paper designs experiments to verify the performance of the method proposed in this paper. Through comparative analysis of experiments, it can be known that the method proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

Fantasy Sports allows individuals to assemble a virtual team to participate in free or paid tournaments and earn rewards. Selecting a good team forms a crucial decision in fantasy cricket. Existing team selection methods cater only to professional cricket and are not suited well to accommodate the differences between fantasy cricket and the on-field game. This paper proposes a two-step methodology for player assessment and team selection in fantasy cricket. Player assessment is carried out using recursive feature elimination in random forest, in which context relevant player metrics are considered and the selection of players is based on modified genetic algorithm. We illustrate the efficacy of the proposed method on Dream11, a popular fantasy sports application. The results show that the proposed method outshines the traditional team selection process in fantasy sports, which is based on hit and trial. Furthermore, we provide a typology to analyse the proposed algorithm along the dimensions of reward distribution and entry fee. [ABSTRACT FROM AUTHOR]

Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems and finding the optimal answer for large models is a computational challenge. Genetic algorithms are a family of metaheuristic algorithms capable of adjusting and redesigning parameters and operations according to the characteristics of ILP models. On the other hand, still the genetic algorithm performs a lot of operations to solve large models, and parallel processing is a suitable technique to tackle this problem. This paper introduces an LP-Relaxation based parallel genetic algorithm that uses a population-based incremental learning technique to presents an expandable solver for large ILP models derived from a behavioral synthesis of digital circuits. In the proposed algorithm, each chromosome provides a state subspace of possible solutions, and each generation is produced based on a probability vector as well as elitism. Our experiments verify the efficiency of the proposed algorithm on multicore platforms, as it outperformed four previous genetic algorithms for solving mixed integer programming problems. The proposed genetic algorithm solved 20 ILP models include up to 5183 int / binary decision variables in less than 20 min using four 16-core AMD Opteron 6386 SE processors. Also, the results indicate that for models with more than 4000 variables, the speedup and the efficiency of the proposed parallel genetic algorithm on 60 CPU cores is more than 18X and 30 % , respectively. [ABSTRACT FROM AUTHOR]

Microservice architectures (MSAs) can increase the performance of distributed systems and enable better resource allocation by sharing underlying resources among multiple microservices (MSs). One of the main advantages of MSAs is the ability to leverage the elasticity provided by an infrastructure so that only the most demanding services are scaled, which can contribute to efficient allocation of processing resources. A major problem in allocating resources to microservices is determining a set of auto-scaling parameters that will result in all microservices meeting specific service level agreements (SLAs). Since the space of feasible configurations can be vast, manually determining a combination of parameter values that will result in all SLAs being met is complex and time consuming. In addition, the performance overhead caused by running microservices concurrently and the overhead caused by the VM instantiation process must also be evaluated. Another problem is that microservices can suffer performance degradation due to resource contention, which depends on how microservices are distributed across servers. To address the aforementioned issues, this paper proposes the modeling of these infrastructures and their auto-scaling mechanisms in a private cloud using stochastic Petri nets (SPNs), the non-dominated sorting genetic algorithm II (NSGA-II), one of the most popular evolutionary algorithms for multiobjective optimization (MOO), and random forest regression (RFR), an ensemble-learning-based method, to identify critical trade-offs between performance and resource consumption considering all deployed MSs. The SPN-based model is capable of representing both instantiation of elastic VMs and a pool of instantiated elastic VMs where only containers are started. The analytical framework enables service providers (SPs) to estimate performance metrics considering configurations that satisfy all performance constraints, use of elastic VMs, discard rate, discard probability, throughput, response time, and corresponding cumulative distribution functions (CDFs). These metrics are critical because they make it possible to estimate the time required to process each request, the number of requests processed in a time interval, the number of requests rejected, and the utilization of resources. The framework was validated with 95% confidence interval (CI) using a real-world testbed. Two case studies were used to investigate its feasibility by evaluating its application in a real scenario. We noticed a significant improvement in performance when using a pool of elastic VMs, where throughput improved by 21.5% and the number of discarded requests decreased by 70%. The application of the framework can help in finding optimized solutions that support both infrastructure planning and online performance prediction, and enable trade-off analyses considering different scenarios and constraints. [ABSTRACT FROM AUTHOR]

This paper presents the experimental observations, theoretical analysis and machine learning model of plastic anisotropy of rolling AA7075-T6. Compression responses have been discussed by obtaining the instantaneous stress–strain relationship. The analytical solution of anisotropic initial yielding and hardening has been derived through generalizing the J2 flow theory and applying the evolutive constitutive parameters. In addition, a machine learning model consisting of artificial neural network optimized by genetic algorithm (GA-ANN) is utilized to simulate the plastic anisotropy of AA7075-T6. According to the comparisons among experimental, theoretical and predicted (GA-ANN) results, the machine learning model provides flexible application and is found easy to be generalized for solving such mechanical problems, but with difficultly in assessment of the model's reliability. Multi-index estimation is a feasible approach to ensure the objective of evaluation in machine learning model. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. [ABSTRACT FROM AUTHOR]

Microstructure determines the most important factors that influence all aspects of the physical properties of the metal. Machine learning based systems allow us to look at the images to find the features of microstructure images which will be useful for classifying such images. These classification outcomes are the fundamental data for many material scientists. However, handcrafted feature vectors extracted by some means may involve a significant amount of irrelevant and redundant features, which may lead to misclassification of the microstructural images. In this paper, at first, a modified version of texture-based feature descriptor, Local Tetra Pattern (LTrP), which is named as Uniform variant of LTrP (ULTrP) is used to extract the features from the microstructural images. Then a feature selection algorithm based on Genetic Algorithm (GA), named as Diversification of Population (DP) in GA (DPGA), is proposed which is applied on ULTrP to remove the possible redundant features present therein. To assess fitness of the candidate solutions, instead of applying a learning algorithm, which is a common trend, the proposed DPGA uses an ensemble of three filter ranking methods. Impressive outcomes obtained by evaluating the proposed classification framework on a standard 7-class microstructural image dataset confirm its superiority over some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate recognition of the type of ground motion is a basic task in the field of seismic engineering. In this paper, the key technologies of detection and recognition of underground seismic signal are studied. The target recognition algorithm is designed to realize the target recognition through denoising the collected target signal and extracting the characteristic quantity. Considering that ground motion signals generated by moving targets on the ground are susceptible to environmental noise, this paper introduces the working principle of wavelet packet denoising and its support vector machine classification model. Wavelet packet was used to transform the signal to denoise first, then zero-crossing rate analysis of the denoised signal was carried out after wavelet packet denoising and extracts the parameters, and the energy of cross-correlation criteria was selected finally. Quantitative indices are combined to construct multi-feature vectors, which are used as input of multi-class support vector machine for training and prediction. In this model, the optimal parameters of support vector machine model are found by genetic algorithm parameter optimization. The experimental results show that the improved model can recognize and classify the ground motion signals caused by people and vehicles correctly and can improve the performance of the classifier. [ABSTRACT FROM AUTHOR]

Traditional polymer processing techniques results in thermal degradation of the desired mechanical properties of a component. Incremental sheet forming (ISF), an innovative flexible cold forming process, can avoid this issue. However, this type of cold processing suffers from serious drawback of spring back due to elastic recovery. Therefore, process control is mandatory to achieve the desired part accuracy. This requires employment of a conducive set of process parameters, thereby turning this into an optimization problem. Using statistical techniques, though beneficial, does not result in an acceptable mean squared error (MSE). In this paper, we explore the use of machine learning methods for error prediction and parameter optimization. Particularly, we propose to use the combination of genetic algorithm and artificial neural network hybrid model for optimized set of parameters that result in a greedy minimized shape error. Experiments on Polypropylene sheet show that the proposed approach results in better optimization compared to statistical technique in minimizing the MSE. [ABSTRACT FROM AUTHOR]

Machine learning for the diagnosis of rotary machines takes priority in generating a training data set through the machine's past data. The training data set uses features that have physical and statistical meaning of vibration signals. A training data is formed on the assumption that the normal condition of the facility is almost similar over time. However, many industrial power plants perform regular O/H (overhaul), and the vibration level of the machine's normal condition is likely to change depending on the O/H results. The vibration level is one of the important factors representing the condition change of rotating machines and is difficult to ignore easily. This paper is a study on a method that can be used for feature-based machine learning with training data formed from past data whose vibration level of the rotating machine has changed due to the influence of maintenance. Data acquisition was made through lab-scale defect simulation test devices, and experimental equipment was simulated before and after O/H with several faults that could occur in rotating machines. The signal named "delta signal" refers to a signal that sets each normal data as a reference signal, matches the fault signal through phase synchronization and resampling, and subtracts it to leave only a difference. The algorithms used in machine learning used genetic algorithm (GA) based feature selection and support vector machines (SVM) for learning and classification. According to the experiment, it was confirmed that in raw signal learning, the similarity by the learned condition (label) decreased due to the influence of maintenance, but the method using delta signal decreased the effect by maintenance, increasing the similarity within the same learned condition. [ABSTRACT FROM AUTHOR]

Object recognition in complex backgrounds has challenged the fields of pattern recognition for years. It is even harder when the targets in images are of different poses. Current methods use descriptors of characteristic vectors and machine learning algorithms to produce classifiers for object recognition. However, the generalization ability of these methods relies on the quality of the training phase and cannot find the precise boundaries of the targets. The geometric features of objects are the most stable and consistent features, so the recognition method based on shapes can be more intuitive than those based on color and texture features. This paper proposes a novel method that uses the images represented by line segments. The recognition mission becomes to effectively filter and combine them. The contour fragments after combinations are expected to satisfy the given model, or certain parts of it. In this way, object recognition can be viewed as a combinatorial optimization problem. This paper develops a genetic algorithm-based method to solve this problem. The experimental results show that this method can solve the combinatorial optimization problem effectively and can accurately distinguish the contour of the target object from the background. This method, which is based on geometric features, may contribute to the development of explicit principals for the description of object structure and recognition method based on symbolic reasoning. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications. Task scheduling and resource provisioning are two important challenges of cloud environment, called cloud resource management. Resource management is a major problem especially for scientific workflows due to their heavy calculations and dependency between their operations. Several algorithms and methods have been developed to manage cloud resources. In this paper, the combination of state-action-reward-state-action learning and genetic algorithm is used to manage cloud resources. At the first step, the intelligent agents schedule the tasks during the learning process by exploring the workflow. Then, in the resource provisioning step, each resource is assigned to an agent, and its utilization is attempted to be maximized in the learning process of its corresponding agent. This is conducted by selecting the most appropriate set of the tasks that maximizes the utilization of the resource. Genetic algorithm is utilized for convergence of the agents of the proposed method, and to achieve global optimization. The fitness function that has been exploited by this genetic algorithm seeks to achieve more efficient resource utilization and better load balancing by observing the deadlines of the tasks. The experimental results show that the proposed algorithm reduces makespan, enhances resource utilization, and improves load balancing, compared to MOHEFT and MCP, the well-known workflow scheduling algorithms of the literature. [ABSTRACT FROM AUTHOR]

In this paper, a systematic approach to achieve optimized design of interior permanent magnet machine (IPM) having novel semi-overlapping windings (NSWs) is presented. The optimization parameters have been determined individually by performing sensitivity analyses. Multi-objective global optimization is subsequently performed. Genetic algorithm (GA) approach, which is also known as "random search with learning algorithm" and an effective optimization tool used for design optimization of electric machines, is employed. IPMs equipped with integer-slot distributed winding (ISDW) and NSW are initially designed by using the geometric and operating parameters of Toyota Prius 2010 IPM. Subsequently, a time-stepping 2D finite element analysis (FEA)-based program is employed to perform the optimization and quickly evaluate the optimal solution among the thousands of design candidates thanks to the sensitivity analyses. In order to reveal the effectiveness and rapidity of the multi-objective global optimization, a comprehensive electromagnetic performance comparison between the original (with ISDWs), initial and optimal designs (with NSWs) is presented. Finally, a small IPM prototype globally optimized by using the proposed procedure is manufactured, and the FEA results have been validated by measurements. Our goals in this study is to advance the state of the art in the multi-objective design optimization of NSW IPMs by performing sensitivity analyses to reach the optimal solution quickly and to determine the most sensitive design parameters affecting the key performance characteristics. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

Failure credibility is popular in measuring safety degree of structure under fuzzy uncertainty due to its excellent property of self-duality. Existing methods for estimating failure credibility can be mainly divided into two categories, i.e., the simulation-based methods and the optimization-based methods. The simulation-based methods are universal and robust, but time-consuming in dealing with high-dimensional problems. The optimization-based methods are efficient and flexible, but the precision of the failure credibility estimate will be impacted by the local optimum solution resulted from some optimization techniques. Thus, in order to overcome the disadvantages of existing methods, an efficient method by combining genetic algorithm and active learning Kriging (AK-GA) is proposed to estimate failure credibility in this paper. Firstly, by use of the fuzzy simulation, the estimation of failure credibility is transformed into finding for the failure/safety sample that processes the maximum joint membership function (MF) from the fuzzy simulation pool, which makes the credibility estimation less difficult to be computed. Secondly, the genetic algorithm is used to find the failure/safety sample with maximum joint MF. Thirdly, to drastically improve the computational efficiency of the proposed method, active learning Kriging is embedded to accurately and efficiently distinguish the failure and safety sample in computing the fitness function of genetic algorithm. Four mathematical and engineering test examples are used to demonstrate the accuracy and efficiency of the proposed AK-GA for estimating failure credibility. [ABSTRACT FROM AUTHOR]

Nowadays, one critical challenge of cybersecurity administrators is the protection of online resources from network intrusions. Despite several academic and industry research initiatives, full protection of online resources from these network intrusions is not feasible. Therefore, several techniques have been developed that use network audit data for accurate detection of network intrusions effectively and efficiently and are used in network intrusion detection systems (NIDSs). But, most of NIDSs reported low detection accuracy with high false alarm rate and provide a single solution that lacks in classification trade-offs. In this paper, the authors present a hybrid approach of multi-objective genetic algorithm and neural networks for creating a set of ensemble solutions for detecting network intrusions effectively. The proposed approach works in two phases that initially creates a set of non-dominating solutions or Pareto optimal solutions of base techniques and then creates ensemble solutions. In the outcome of individual solutions or models in the ensemble are aggregated using most popular method of majority voting. The proposed hybrid approach is evaluated using benchmark datasets of NSL_KDD and ISCX-2012 datasets for intrusion detection. The evaluation results using benchmark datasets demonstrate that the proposed hybrid approach enables detecting network intrusions effectively as compared to conventional ensemble approaches, namely bagging and boosting. The resultant ensemble solutions are non-dominating and provide classification trade-offs for cybersecurity administrators. The results also show that the proposed hybrid approach detects both minority and majority intrusion types accurately. The proposed hybrid approach demonstrated a detection accuracy of 97% and 88% with FPR of 2.4% and 2% for ISCX-2012 and NSL_KDD datasets, respectively. [ABSTRACT FROM AUTHOR]

Soccer is one of the most played sports in the world with many individuals involved. Recognizing talented players and team selection is a challenging task for coaches. Coaches need to employ different methods in order to rank soccer players and select them by their corresponding rank. In this paper, we propose a new web-based approach for ranking soccer players by using information available from online sources. The first step to do this task is collecting information about players. This information is fetched from the Internet and will be preprocessed or augmented by professional users at a web-based expert system. Information is highly dynamic in a sense that data change constantly. To build a ranking system for players, machine learning approaches are employed. We use different classification algorithms on prepared data and choose the best model from applied methods to rank new players in each state of the dataset. To improve classification results, a weighted ensemble method using a genetic algorithm for optimizing weights is proposed. We used this model to predict players' rank. The ranking is done separately for different types of ranks with two, three, or four number of rankings. Experiments were done in the Persian premier league and have shown promising results for predicting player ranks with improvement in accuracy for four-, three-, and two-class predictions. The results show that (1) achieving higher performance will be harder with each level of granularity that is added to ranking classes of system. (2) A web-based system can be useful in order to develop a ranking system in sports. (3) The new ensemble method is able to improve classification models by improving the best model. We believe that using our innovative system, challenges for team selection and talent recognition can be solved. This assumption is proved with final results of the system and feedbacks from professionals. [ABSTRACT FROM AUTHOR]

Evolutionary K-Means (EKM), which combines K-Means and genetic algorithm, solves K-Means' initiation problem by selecting parameters automatically through the evolution of partitions. Currently, EKM algorithms usually choose silhouette index as cluster validity index, and they are effective in clustering well-separated clusters. However, their performance of clustering noisy data is often disappointing. On the other hand, clustering stability-based approaches are more robust to noise; yet, they should start intelligently to find some challenging clusters. It is necessary to join EKM with clustering stability-based analysis. In this paper, we present a novel EKM algorithm that uses clustering stability to evaluate partitions. We firstly introduce two weighted aggregated consensus matrices, positive aggregated consensus matrix (PA) and negative aggregated consensus matrix (NA), to store clustering tendency for each pair of instances. Specifically, PA stores the tendency of sharing the same label and NA stores that of having different labels. Based upon the matrices, clusters and partitions can be evaluated from the view of clustering stability. Then, we propose a clustering stability-based EKM algorithm CSEKM that evolves partitions and the aggregated matrices simultaneously. To evaluate the algorithm's performance, we compare it with an EKM algorithm, two consensus clustering algorithms, a clustering stability-based algorithm and a multi-index-based clustering approach. Experimental results on a series of artificial datasets, two simulated datasets and eight UCI datasets suggest CSEKM is more robust to noise. [ABSTRACT FROM AUTHOR]

Procedural animation systems are capable of synthesising life-like organic motion automatically. However, due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective and the process is difficult to automate effectively. In this paper, we describe a crowd-sourced approach to procedural animation parameter optimisation using genetic algorithms. We test our approach by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criterion. Our results show that within a few generations a group of users can successfully tune the system towards a desired behaviour. Our secondary motivation is to investigate whether there are differences in animation and behavioural preference between observations made using a standard desktop monitor and those made in virtual reality (VR). We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour. [ABSTRACT FROM AUTHOR]

This paper implemented an artificial neural network (ANN) on a field programmable gate array (FPGA) chip for Mandarin speech measurement and recognition of nonspecific speaker. A three-layer hybrid learning algorithm (HLA), which combines genetic algorithm (GA) and steepest descent method, was proposed to fulfill a faster global search of optimal weights in ANN. Some other popular evolutionary algorithms, such as differential evolution, particle swarm optimization and improve GA, were compared to the proposed HLA. It can be seen that the proposed HLA algorithm outperforms the other algorithms. Finally, the designed system was implemented on an FPGA chip with an SOC architecture to measure and recognize the speech signals. [ABSTRACT FROM AUTHOR]