Genetic Algorithm In Conjunction Research Articles

Exploring the Future of Stock Market Prediction through Machine Learning: An Extensive Review and Outlook

A thorough analysis of trends and future directions reveals how machine learning is revolutionizing stock market forecasting. The most recent research on machine learning applications for stock market prediction during the previous 20 years is methodically reviewed in this article. Artificial neural networks, support vector machines, genetic algorithms in conjunction with other methodologies, and hybrid or alternative AI approaches were the categories used to group journal articles. Every category was examined to identify trends, distinct perspectives, constraints, and areas that needed more research. The results provide insightful analysis and suggestions for further study in this developing topic.

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

Solving the Cauchy Problem Related to the Helmholtz Equation through a Genetic Algorithm

The Cauchy problem associated with the Helmholtz equation is an ill-posed inverse problem that is challenging to solve due to its instability and sensitivity to noise. In this paper, we propose a metaheuristic approach to solve this problem using Genetic Algorithms in conjunction with Tikhonov regularization. Our approach is able to produce stable, convergent, and accurate solutions for the Cauchy problem, even in the presence of noise. Numerical results on both regular and irregular domains show the effectiveness and accuracy of our approach.

Generation and optimization of cross-sections for electron-C4F7N collisions

Abstract A complete and consistent set of electron-neutral collision cross-sections for the novel insulating gas C4F7N is reported. The set is composed of a combination of cross-sections previously reported in literature, optimized via a genetic algorithm in conjunction with a multi-term Boltzmann equation solver, and calculated ab initio using the R-matrix code Quantemol-EC. The finalized set accurately reproduces reported macroscopic rate and transport coefficients as well as Townsend coefficients and critical electric field strengths in C4F7N and its mixtures with nitrogen, carbon dioxide, and argon.

A review of practice of using evolutionary algorithms for neural network synthesis and training

The object of this research is the application of evolutionary algorithms for the synthesis and training of neural networks. The paper aims to select and review the existing experience on using evolutionary algorithms as competitive methods to conventional approaches in neural network training and creation, and to evaluate such existing solutions for further development of this field. The essence of the obtained results lies in the successful application of genetic algorithms in conjunction with neural networks to optimize parameters, architecture, and weight coefficients of the networks. The genetic algorithms allowed improving the performance and accuracy of neural networks, especially in cases where backpropagation algorithms faced difficulties in finding optimal solutions. These results can be attributed to the fact that genetic algorithms are efficient methods for global optimization in parameter space. They help avoid local minima and discover more reliable and stable solutions. The obtained findings can be practically utilized to enhance the performance and quality of neural networks in various classification and prediction tasks. The use of genetic algorithms enables the selection of optimal weight coefficients, network connections, and identification of significant features from the dataset. However, they come with the limitation of additional time costs for evaluating the entire population according to the selection criteria. It is worth noting that the application of genetic algorithms is not a universal method for all tasks, and the algorithm parameters should be individually tuned for each specific problem. Further research could focus on refining the combination methods of genetic algorithms and neural networks, as well as exploring their application in new domains and tasks.

An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems

The university course timetable problem (UCTP) is known to be NP-hard, with solution complexity growing exponentially with the problem size. This paper introduces an algorithm that effectively tackles UCTPs by employing a combination of exploration and exploitation strategies. The algorithm comprises two main components. Firstly, it utilizes a genetic algorithm (GA) to explore the search space and discover a solution within the global optimum region. Secondly, it enhances the solution by exploiting the region using an iterated local search (ILS) algorithm. The algorithm is tested on two common variants of UCTP: the post-enrollment-based course timetable problem (PE-CTP) and the curriculum-based course timetable problem (CB-CTP). The computational results demonstrate that the proposed algorithm yields competitive outcomes when compared empirically against other existing algorithms. Furthermore, a t-test comparison with state-of-the-art algorithms is conducted. The experimental findings also highlight that the hybrid approach effectively overcomes the limitation of local optima, which is encountered when solely employing GA in conjunction with local search.

Design and analysis of a triangular bi-axial flexure hinge

To achieve the requirement of flexure hinges for specific optical precision equipment, a new triangular bi-axial flexure hinge is proposed. The analysis model for the flexibility of the triangular bi-axial flexure hinges is derived. After that, the linear and nonlinear finite element methods are used to verify the analysis model. Then, the physical dimensions of the hinge are optimized using the multi-island genetic algorithm in conjunction with the finite element method, and the rotational stiffness and center drift are diminished. Static analysis and modal analysis of the hinge are conducted. A test system was built to gage the flexure hinge’s rotational stiffness. The results demonstrated that there was good agreement between the analytically calculated value, simulated calculated value, and experimental value. To summarize, the analysis model met the design requirements of the triangular bi-axial flexible hinge, and the multi-island genetic algorithm effectively optimized the physical dimensions of the hinge and enhanced its performance. The design process provides new ideas for the design of other forms of hinges.

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Progesterone receptors (PRs) are implicated in various cancers since their presence/absence can determine clinical outcomes. The overstimulation of progesterone can facilitate oncogenesis and thus, its modulation through PR inhibition is urgently needed. To address this issue, a novel stacked ensemble learning approach (termed StackPR) is presented for fast, accurate, and large-scale identification of PR antagonists using only SMILES notation without the need for 3D structural information. We employed six popular machine learning (ML) algorithms (i.e., logistic regression, partial least squares, k-nearest neighbor, support vector machine, extremely randomized trees, and random forest) coupled with twelve conventional molecular descriptors to create 72 baseline models. Then, a genetic algorithm in conjunction with the self-assessment-report approach was utilized to determine m out of the 72 baseline models as means of developing the final meta-predictor using the stacking strategy and tenfold cross-validation test. Experimental results on the independent test dataset show that StackPR achieved impressive predictive performance with an accuracy of 0.966 and Matthew’s coefficient correlation of 0.925. In addition, analysis based on the SHapley Additive exPlanation algorithm and molecular docking indicates that aliphatic hydrocarbons and nitrogen-containing substructures were the most important features for having PR antagonist activity. Finally, we implemented an online webserver using StackPR, which is freely accessible at http://pmlabstack.pythonanywhere.com/StackPR. StackPR is anticipated to be a powerful computational tool for the large-scale identification of unknown PR antagonist candidates for follow-up experimental validation.

Systems of Systems (SoS) meta‐architecture evaluation utilizing the SoS Explorer for cyber‐physical smart solar roadways

Systems of Systems (SoS) meta‐architecture evaluation utilizing the SoS Explorer for cyber‐physical smart solar roadways

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins

Thermophilic proteins (TPPs) are important in the field of protein biochemistry and development of new enzymes. Thus, computational methods must be urgently developed to accurately and rapidly identify TPPs. To date, several computational methods have been developed for TPP identification; however, few limitations in terms of performance and utility remain. In this study, we present a novel computational method, SAPPHIRE, to achieve more accurate identification of TPPs using only sequence information without any need for structural information. We combined twelve different feature encodings representing different perspectives and six popular machine learning algorithms to train 72 baseline models and extract the key information of TPPs. Subsequently, the informative predicted probabilities from the baseline models were mined and selected using a genetic algorithm in conjunction with a self-assessment-report approach. Finally, the final meta-predictor, SAPPHIRE, was built and optimized by applying an optimal feature set. The performance of SAPPHIRE in the 10-fold cross-validation test showed that a superior predictive performance compared with several baseline models could be achieved. Moreover, SAPPHIRE yielded an accuracy of 0.942 and Matthew's coefficient correlation of 0.884, which were 7.68 and 5.12% higher than those of the current existing methods, respectively, as indicated by the independent test. The proposed computational approach is anticipated to facilitate large-scale identification of TPPs and accelerate their applications in the food industry. The codes and datasets are available at https://github.com/plenoi/SAPPHIRE.

Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques.

Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all around the world, it has now become a huge issue and a threat to their safety and wellbeing. Breast cancer is one of the major causes of death among females all over the globe, and it is particularly prevalent in the United States. It is possible to diagnose breast cancer using a variety of imaging modalities including mammography, computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and biopsies, among others. To analyze the picture, a histopathology study (biopsy) is often performed, which assists in the diagnosis of breast cancer. The goal of this study is to develop improved strategies for various CAD phases that will play a critical role in minimizing the variability gap between and among observers. It created an automatic segmentation approach that is then followed by self-driven post-processing activities to successfully identify the Fourier Transform based Segmentation in the CAD system to improve its performance. When compared to existing techniques, the proposed segmentation technique has several advantages: spatial information is incorporated, there is no need to set any initial parameters beforehand, it is independent of magnification, it automatically determines the inputs for morphological operations to enhance segmented images so that pathologists can analyze the image with greater clarity, and it is fast. Extensive tests were conducted to determine the most effective feature extraction techniques and to investigate how textural, morphological, and graph characteristics impact the accuracy of categorization classification. In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with a Convolutional Neural Network Classifier. The practical application of the suggested improved segmentation and classification algorithms for the CAD framework may reduce the number of incorrect diagnoses and increase the accuracy of classification. So, it may serve as a second opinion tool for pathologists and aid in the early detection of diseases.

An adaptive artificial neural network-based generative design method for layout designs

Layout designs are encountered in a variety of fields. For problems with many design degrees of freedom, efficiency of design methods becomes a major concern. In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process. A main issue of many such approaches is the need for a large corpus of training data that are generated using high-dimensional simulations. The high computational cost associated with training data generation largely diminishes the efficiency gained by using machine learning methods. In this work, an adaptive artificial neural network-based generative design approach is proposed and developed. This method uses a generative adversarial network to generate design candidates and thus the number of design variables is greatly reduced. To speed up the evaluation of the objective function, a convolutional neural network is constructed as the surrogate model for function evaluation. The inverse design is carried out using the genetic algorithm in conjunction with two neural networks. A novel adaptive learning and optimization strategy is proposed, which allows the design space to be effectively explored for the search for optimal solutions. As such the number of training data needed is greatly reduced. The performance of the proposed design method is demonstrated on two heat source layout design problems. In both problems, optimal designs have been obtained. Compared with several existing approaches, the proposed approach has the best performance in terms of accuracy and efficiency.

The Walking Robot Control System that is Adaptive to Changes in the Kinematics

The walking mobile robots (WMR) have recently become widely popular in robotics. They are especially useful in the extreme cases: search and rescue operations; cargo delivery over highly rough terrain; building a map. These robots also serve to explore and describe a partially or completely non-deterministic workspace, as well as to explore areas that are dangerous to human life. One of the main requirements for these WMR is the robustness of its control system. It allows WMR to maintain the operability when the characteristics of the support surface change as well as under more severe conditions, in particular, loss of controllability or damage of the supporting limb (SL). We propose to use the principles of genetic programming to create a WMR control system that allows a robot to adapt to possible changes in its kinematics, as well as to the characteristics of the support surface on which it moves. This approach does not require strong computational power or a strict formal classification of possible damage to the WMR. This article discusses two main WMR control modes: standard, which accord to a serviceable kinematics, and emergency, in which one or more SL drives are damaged or lost controllability. As an example, the structure of the control system of the WMP is proposed, the kinematics of which is partially destroyed in the process of movement. We developed a method for controlling such robot, which is based on the use of a genetic algorithm in conjunction with the Mealy machine. Modeling of modes of movement of WMR with six SL was carried out in the V-REP program for two cases of injury: absent and not functioning limb. We present the results of simulation of emergency gaits for these configurations of WMP and the effectiveness of the proposed method in the case of damage to the kinematic scheme. We also compared the performance of the genetic algorithm for the damaged WMR with the standard control algorithm.

Genetic Algorithm-Based Optimal Design of a Rolling-Flying Vehicle

Abstract This work describes a design optimization framework for a rolling-flying vehicle consisting of a conventional quadrotor configuration with passive wheels. For a baseline comparison, the optimization approach is also applied for a conventional (flight-only) quadrotor. Pareto-optimal vehicles with maximum range and minimum size are created using a hybrid multi-objective genetic algorithm in conjunction with multi-physics system models. A low Reynolds number blade element momentum theory aerodynamic model is used with a brushless DC motor model, a terramechanics model, and a vehicle dynamics model to simulate the vehicle range under any operating angle-of-attack and forward velocity. To understand the tradeoff between vehicle size and operating range, variations in Pareto-optimal designs are presented as functions of vehicle size. A sensitivity analysis is used to better understand the impact of deviating from the optimal vehicle design variables. This work builds on current approaches in quadrotor optimization by leveraging a variety of models and formulations from the literature and demonstrating the implementation of various design constraints. It also improves upon current ad hoc rolling-flying vehicle designs created in previous studies. Results show the importance of accounting for oft-neglected component constraints in the design of high-range quadrotor vehicles. The optimal vehicle mechanical configuration is shown to be independent of operating point, stressing the importance of a well-matched, optimized propulsion system. By emphasizing key constraints that affect the maximum and nominal vehicle operating points, an optimization framework is constructed that can be used for rolling-flying vehicles and conventional multi-rotors.

A novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites

The paper presents a novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites. The proposed method uses the Chebyshev Neural Network and genetic algorithm in conjunction with satellite attitude rate data only. A data-driven model is first developed that fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. Next, the data is trained using Chebyshev Neural Network. An adaptive threshold-based fault detection algorithm is applied to detect the faults in the spin and gimbal motors of the control moment gyroscopes. A fault isolation scheme is developed wherein an objective function is optimized using a genetic algorithm for different cases of system parameters. The proposed scheme has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. Overall, the proposed methodology can be regarded as a promising fault diagnostic tool for satellites using limited historical data and measurements.

Elucidation of Bottom-Up Growth of CaCO3 Involving Prenucleation Clusters from Structure Predictions and Decomposition of Globally Optimized (CaCO3)n Nanoclusters.

Low-energy minima structures for (CaCO3)n, n ≤ 28, are predicted using bottom-up genetic algorithms in conjunction with density functional theory electronic structure calculations, in comparison with the frozen and relaxed top-down clusters generated by cuts from the calcite, vaterite, and aragonite crystal structures. Similarities in structural motifs for the bottom-up and relaxed top-down are revealed using a fragment recognition technique. Fragment energy decomposition analysis shows that the bottom-up and relaxed top-down clusters belong to two classes of amorphous clusters with distinct intracluster energy distributions, despite their structural similarity. The bottom-up clusters with >20 formula units are surface stabilized with negative surface energy densities. In contrast, the top-down clusters are interior stabilized with positive surface energy densities. We prove that the sign of the surface energy density determines whether the nucleation reaction energy as a function of nuclear size has a maximum or a minimum. The surface-stabilized bottom-up clusters are proposed to be a type of prenucleation cluster at the minimum of the nucleation reaction energy. A mechanism for mineralization of CaCO3 involving prenucleation clusters and nonclassical growth pathway is proposed on the basis of our theoretical findings, which is consistent with previous titration experiments.

Enhancing the curvature mode shape method for structural damage severity estimation by means of the distributed genetic algorithm

An important challenge in damage detection based on the curvature mode shape (CMS) method is recognizing damage severity in damaged structures. Here, an efficient approach is proposed based on the finite element (FE) method, which detects the existence, location and severity of damage using the distributed genetic algorithm in conjunction with CMS. First, using the mode shape of the structure, curvature is calculated at the nodes of the structure in the FE model. By comparing the curvature between the intact and damaged structures, the existence and location of damage are determined. Next, by defining an optimization problem, damage severity in the damaged elements is determined. Results of applying the proposed approach on beam and frame structures under various damage scenarios and an experimental set-up show that this method is very effective in damage detection of such structures. Furthermore, the performance and accuracy of the proposed method are examined for various noise levels.

Binary Nanoalloy Clusters: A Case Study for FeCr Clusters.

A study of FexCry binary nanoalloy clusters is carried out for sizes n=x+y=13 and 55, the first two closed shell structures. While for n=13, various compositions like x=12 and y=1; x=11 and y=2; x=10 and y=3 ; x=1 and y=12 are studied, for the larger size (n=55) the morphology of a Fe-rich cluster (Fe42Cr13) and a Cr-rich cluster (Fe13Cr42) are analysed. The potential energy surface of the nanoalloys are scanned thoroughly using an evolutionary genetic algorithm in conjunction with a density functional theory based first principles method and the most stable clusters are picked up for every size and composition. A definite hierarchy in Cr-Cr,Fe-Cr and Fe-Fe bonding strengths is seen to determine the stability of the clusters. Cr-Cr bonding is promoted over Fe-Cr bonding which in turn is preferred over Fe-Fe bonding in the nanoalloy systems.

A comparison of accelerometer and piezofilm-based force balances for hypersonic shock tunnels

A blunt double-cone model, equipped with a three-component accelerometer or piezofilm-based force balance system, is tested in the IITB-Shock Tunnel at 0 º and 10 º angle of inclinations during force measurement experiments. Conventional accelerometer force balance theory and soft computing-based adaptive neuro fuzzy inference system have been adopted to recover the axial and normal forces. In order to arrive at the time varying forces, these balances are calibrated using impulse hammer tests at single or multiple points. Necessity of multipoint calibration has also been demonstrated during recovery of forces for both the balances. Genetic algorithm in conjunction with adaptive neuro fuzzy inference system has been implemented herein to train the system for force prediction. Encouraging agreement has been noted between the predictions made using accelerometer force balance theory and adaptive neuro fuzzy inference system-based recovery of forces from accelerometer and piezofilm balances. Thus, present studies not only provide the methodology for calibration and implementation of piezofilm based force measurement, but also recommend its usage in short duration impulse facilities.

Shape Optimization and Sensitivity Analysis of a Morphing-Wing Aircraft

Morphing-wing concepts have received growing interests in the recent years for enhancing the multi-mission performance of unmanned aerial vehicle. However, to obtain a feasible morphing strategy, the optimization design studies are required. In this paper, an aerodynamic optimization study for a morphing-wing aircraft with variable sweep, span, and chord length was conducted to obtain the optimum configurations at subsonic, transonic and supersonic conditions. The optimization objective is to obtain maximum lift-to-drag ratios subject to lift coefficients and static stability constraints at each flight condition. A genetic algorithm in conjunction with surrogate models was employed to search optimum solutions in the entire design space. The aerodynamic forces are calculated by an Euler-based solver and friction drag estimation code. The optimum configuration corresponding to any flight condition can be determined through the optimization. A global sensitivity analysis based on the surrogate model was also carried out, hence the contribution of each design variable to the optimization objective can be analyzed. The results indicated that the optimum wing at subsonic speeds have a lower sweep angle and high aspect ratio, and the wing sweep has a primary contribution to lift-to-drag ratios, and the span is secondary; at transonic conditions, the medium-sweep wing is the optimum, and the contribution of the span is far more than other variables; at supersonic conditions, the optimum configuration becomes a high-sweep cropped delta wing, and the span has a dominant contribution, and the sweep is secondary.