Genetic Algorithm-based Method Research Articles

Optimized array for powering sensors: Thermal and electromagnetic energy harvesting and fusion

Environmental energy harvesting technology has achieved great success in remote sensor power supply applications. However, existing technologies in the power grid neglect the potential of weak energy sources. This study aims to optimize the most reasonable array configurations and create hybrid energy fusion schemes for different weak energy environments. Taking thermal and electromagnetic energy harvesting from cable surfaces and currents in coaxial cables as an example, this paper introduces a genetic algorithm-based optimization method of harvester array configuration under multiple constraints and a dual-source energy fusion system. First, a vector impedance parameter identification method based on the equivalent circuit model of current transformer (CT) is proposed. Later, an optimization model for harvester array is established. Then, a genetic algorithm is used to optimize array combinations. And, a dual-source energy fusion system is proposed. Finally, thermal and electromagnetic energy harvesting and fusion are performed using 3 × 1 TEGs array and 6 × 1CT array. Under the specified conditions of a hot end temperature of 50 ℃ and an effective current value of 2 A, the power supply for the Zigbee wireless sensing system is realized to monitor the temperature of the cable core.

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions

Background and Objective:One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user’s skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. Methods:The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. Results:The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. Conclusions:The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players’ emotional or physiological states.

A Learner-Centric Explainable Educational Metaverse for Cyber–Physical Systems Engineering

Cyber–physical systems have become critical across industries. They have driven investments in education services to develop well-trained engineers. Education services for cyber–physical systems require the hiring of expert tutors with multidisciplinary knowledge, as well as acquiring expensive facilities/equipment. In response to the challenges posed by the need for the equipment and facilities, a metaverse-based education service that incorporates digital twins has been explored as a solution. However, the issue of recruiting expert tutors who can enhance students’ achievements remains unresolved, making it difficult to effectively cultivate talent. This paper proposes a reference architecture for a learner-centric educational metaverse with an intelligent tutoring framework as its core feature to address these issues. We develop a novel explainable artificial intelligence scheme for multi-class object detection models to assess learners’ achievements within the intelligent tutoring framework. Additionally, a genetic algorithm-based improvement search method is applied to the framework to derive personalized feedback. The proposed metaverse architecture and framework are evaluated through a case study on drone education. The experimental results show that the explainable AI scheme demonstrates an approximately 30% improvement in the explanation accuracy compared to existing methods. The survey results indicate that over 70% of learners significantly improved their skills based on the provided feedback.

Task Offloading and Execution in Edge-Cloud Collaborative Network Using Genetic Algorithm

Mobile Edge Computing (MEC) system is now one of the most emerging sectors in wireless communication. It provides computation capability near the edge users and reduces the dependency on the Internet Cloud (IC). The edge users offload the task to the MEC server due to the lack of computing resources for executing resource-hungry applications. MEC servers can serve faster as they are quite close to the edge users but have limited computation resources compared to IC. On the other hand, IC has abundant computation resources but offloading to IC will add extra latency to complete tasks execution. In this scenario, an MEC has to decide intelligently which tasks should be executed by itself and which should be sent to IC. In this paper, we addressed this issue of task assignment and execution either on MEC or on IC and formulated an optimization problem to reduce the task processing latency. The problem is Integer Linear and NP-Complete, thus having higher time complexity. In this regard, we proposed a Genetic algorithm-based Meta-Heuristic method to solve the task completion problem considering the delay constraint of the user. Simulation results of the proposed method indicated improvement in terms of successful task execution, task turnaround time, and better Quality of experience (QoE) compared to the state-of-the-art works. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 42-51

Differential learning model for carbon emission allowance prices prediction based on adaptive decomposition and joint feature contribution

Currently, how to effectively control carbon emissions is a global concern. Accurate carbon price prediction can help us more effectively control carbon emissions and reduce environmental pollution. Due to the complexity of carbon valence, this study proposes an improved decomposition ensemble prediction model. Firstly, a novel signal decomposition algorithm is designed to adaptively determine the number of decomposition modes for carbon price sequences, obtaining a set of intrinsic mode functions (IMFs). Secondly, a creative method for assessing the contribution of feature components is developed. It utilizes a genetic algorithm-based mutual-information method to calculate the contribution of each IMFs to the original sequence, classifying all feature components into high and low contribution components. Then, this study developed a differential learning method that partitions the contribution of feature components and applies targeted learning and integration using temporal convolutional network with different structures. Compared to traditional forecasting frameworks, this framework improves prediction performance while reducing structural complexity and computational costs. Empirical results demonstrate the superiority and robustness of the proposed model with the outcomes of this study present the performances that the proposed model outperforms the others with Guangzhou's mean absolute percentage error of 3.30313%, which are beneficial for governments and enterprises to predict adverse signals in the carbon market in a more timely manner, and to take measures in advance to maintain the stability of carbon emissions.

A novel method of internal ballistics identification and performance prediction for SRMs based on genetic algorithm

Improving the identification accuracy of internal ballistic parameters in the solid rocket motor(SRM) is of great significance in guaranteeing that missiles fulfill their intended operational missions. In practice, the internal ballistic performance is according to the inverse calculation burning area obtained by the measured pressure data of the SRM and the measured burning rate, which still has ascending space for optimization in the prediction accuracy. Accordingly, a genetic algorithm-based method for the identification of internal ballistic parameters and performance prediction for SRMs was proposed. Based on the measured, data of limited test runs, the initial identification of the burning rate coefficient, pressure exponent and propellant density was carried out by GA (Genetic Algorithm). The model was updated on the basis of the inverse calculation burning area obtained by identification results. Then the secondary identification was carried out to modify the key parameters. The Φ50mm laboratory-scale test SRM was analyzed as an example. The internal ballistic performance in the SRM was predicted. The calculation results show that the prediction results obtained by the method are in high agreement with the measured pressure data, which verifies the effectiveness of the method in improving the prediction accuracy of the internal ballistic performance.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

Power grid planning and optimal dispatch based on intelligent optimization and genetic algorithm

Power grid planning is a prerequisite for optimal design, operation, and planning of the power grid. Based on the characteristics of the power system, this article explored a genetic algorithm-based power network optimization method. In genetic algorithms, simulated annealing algorithms were used to optimize candidate individuals, resulting in the optimal ratio of the number of selected individuals to the number of all mutated individuals. At the same time, a simulated annealing algorithm was added to the optimization process to improve the shortcomings of GA (Genetic Algorithm). When the genetic algorithm selected individuals from the parent population, a simulated annealing algorithm was used to optimize candidate individuals, making the ratio of the number of selected individuals to the number of all mutated individuals optimal. In order to overcome the shortcomings of GA, the tabu search algorithm was introduced into the optimization process, making it have better optimization ability. Through optimization experiments on 15 nodes, it was found that this method had good convergence speed. The final experiment also proved that the power planning based on the simulated annealing genetic algorithm was superior to other models (the load balance, power supply capacity matching, and coordination index scores of the substation based on the algorithm model in this article were 0.5111, 2.2693, and 120.215, respectively, which were higher than other schemes).

Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters

A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India. Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for the second and third trimesters. The gold standard for GA estimation in the first trimester was determined using ultrasonography. The Garbhini-GA2, a polynomial regression model, was developed using the genetic algorithm-based method, showcasing the best performance among the models considered. This model incorporated three of the five routinely measured ultrasonographic parameters during the second and third trimesters. To assess its performance, the Garbhini-GA2 model was compared against the Hadlock and INTERGROWTH-21st models using both the TEST set (N=1493) from the GARBH-Ini cohort and an independent VALIDATION dataset (N=948) from the Christian Medical College (CMC), Vellore cohort. Evaluation metrics, including root-mean-squared error, bias, and preterm birth (PTB) rates, were utilised to comprehensively assess the model's accuracy and reliability. With first trimester GA dating as the baseline, Garbhini-GA2 reduced the GA estimation median error by more than three times compared to the Hadlock formula. Further, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to the INTERGROWTH-21st and Hadlock formulae, which overestimated the rate by 22.47% and 58.91%, respectively. The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and validated using Indian population data. Its higher accuracy in GA estimation, comparable to GA estimation in the first trimester and PTB classification, underscores the significance of deploying population-specific GA formulae to enhance antenatal care. The GARBH-Ini cohort study was funded by the Department of Biotechnology, Government of India (BT/PR9983/MED/97/194/2013). The ultrasound repository was partly supported by the Grand Challenges India-All Children Thriving Program, Biotechnology Industry Research Assistance Council, Department of Biotechnology, Government of India (BIRAC/GCI/0115/03/14-ACT). The research reported in this publication was made possible by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC. The external validation study at CMC Vellore was partly supported by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC and by Exploratory Research Grant (SB/20-21/0602/BT/RBCX/008481) from Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. An alum endowment from Prakash Arunachalam (BIO/18-19/304/ALUM/KARH) partly funded this study at the Centre for Integrative Biology and Systems Medicine, IIT Madras.

A random-key genetic algorithm-based method for transportation network vulnerability envelope analysis under simultaneous multi-link disruptions

Transportation network vulnerability envelope (TNVE), constituted by the upper and lower bounds of network performance among all possible disruption scenarios, has recently been proposed as a systematic tool to characterize the impact of simultaneous disruptions of multiple links in a transportation network. Both pessimistic and optimistic cases and the possible range of disruption consequences can be revealed by the TNVE, which can be modeled as a unified optimization problem without the need to enumerate and evaluate all possible disruption scenarios. Specifically, the TNVE problem can be formulated as a binary integer bi-level program (BLP), in which the upper-level problem maximizes/minimizes the remaining network performance under a given number of disrupted links, and the lower-level problem adopts the shortest path problem to check the post-disruption connectivity of each origin–destination (O-D) pair while circumventing the cumbersome path enumeration or path set pre-generation. However, the binary integer BLP is computationally intractable, which hinders the applications of TNVE in practice. This study aims to develop an efficient method based on the random key genetic algorithm (RKGA) for determining the TNVE under simultaneous multi-link disruptions. The main features and benefits of the proposed method include: (a) it simultaneously solves the upper- and lower-bound problems at the same time while guaranteeing the feasibility of all solutions in the solution procedure; (b) it improves the computational efficiency to ensure the applicability to real transportation networks; and (c) it can provide a variety of alternative solutions in addition to the single optimal one, which facilitates the derivation of TNVE buffer and identification of sub-important links. These benefits make the proposed method efficient and effective for solving the TNVE problem. The applicability of the proposed method is demonstrated with small and medium-sized networks, as well as a large-scale real road network. Numerical experiments are conducted to illustrate the usage of TNVE for vulnerability analysis of transportation networks.

Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton.

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation.

A New Last Mile Delivery Approach for the Hybrid Truck Multi-Drone Problem Using a Genetic Algorithm

In this study, the use of drones in transportation and some optimization studies carried out in the literature so far were examined. Based on these investigations, a genetic algorithm-based method has been proposed to solve the hybrid truck multi-drone problem. With the proposed algorithm, scenarios have been created using single or multiple drones and one truck for a certain number of customers to be delivered, and results have been obtained. In these scenarios, the main aim is to minimize delivery time. The results obtained have also been compared with other optimization algorithms and other results in the literature, and in addition, it has been proven that there is an inverse relationship between the number of drones and the delivery time. As a result of the comparisons, it has been clearly seen that the proposed algorithm is quite successful in finding the most suitable route in the shortest time compared to other studies. A solution has been created for a real-world problem with the proposed genetic algorithm-based algorithm, and it has been concluded that it is quite successful here as well.

Optimizing H.266/VVC Intra Coding with a Genetic Algorithm: Balancing Speed and Quality

The growing need for high-definition video material requires improvements in video encoding systems that maximize encoding performance while simultaneously improving compression efficiency. This paper presents a novel genetic algorithm-based intra-coding optimization method for the H.266Versatile Video Coding (VVC) standard. One of the biggest problems in video compression is finding the ideal balance between encoding speed and video quality, which is what our approach aims to solve. Our suggested method makes use of the strong search capabilities of the evolutionary algorithm to choose the best Multi-Type Tree (MTT) partitions and coding tools from the wide range of possibilities present in H.266VVC. The wellness assessment work that guides this choice method combines criteria for perceptual appraisal of video quality and measures for coding productivity appraisal.

Genetic Algorithm-Based Method for Discovering Involutory MDS Matrices

In this paper, we present an innovative approach for the discovery of involutory maximum distance separable (MDS) matrices over finite fields F2q, derived from MDS self-dual codes, by employing a technique based on genetic algorithms. The significance of involutory MDS matrices lies in their unique properties, making them valuable in various applications, particularly in coding theory and cryptography. We propose a genetic algorithm-based method that efficiently searches for involutory MDS matrices, ensuring their self-duality and maximization of distances between code words. By leveraging the genetic algorithm’s ability to evolve solutions over generations, our approach automates the process of identifying optimal involutory MDS matrices. Through comprehensive experiments, we demonstrate the effectiveness of our method and also unveil essential insights into automorphism groups within MDS self-dual codes. These findings hold promise for practical applications and extend the horizons of knowledge in both coding theory and cryptographic systems.

A feature selection based on genetic algorithm for intrusion detection of industrial control systems

With the popularity of Internet technology, industrial control systems (ICS) have started to access the Internet, which significantly facilitates engineers to manage ICS remotely but also exposes risks. Usually, an intrusion detection system (IDS) is used to secure network systems. Feature selection plays a crucial role in IDSs because detecting anomalies from high-dimensional network traffic features is time-consuming. However, few specific studies have been conducted for ICS. Many redundant features and data imbalance problems in ICS data lead to poor performance and low efficiency of generic IDS classification. In this paper, we design a genetic algorithm-based feature selection method for ICS characteristics. The proposed method incorporates a feature ranking fusion mechanism in the genetic algorithm for eliminating redundant features, enhances the global merit-seeking speed using the growing tree clustering idea, and we also design a new fitness function for ICS characteristics. The effectiveness and advancement of the proposed method are demonstrated on a real ICS dataset.

Using AI to decode the behavioral responses of an insect to chemical stimuli: towards machine-animal computational technologies

Orthoptera are insects with excellent olfactory sense abilities due to their antennae richly equipped with receptors. This makes them interesting model organisms to be used as biosensors for environmental and agricultural monitoring. Herein, we investigated if the house cricket Acheta domesticus can be used to detect different chemical cues by examining the movements of their antennae and attempting to identify specific antennal displays associated to different chemical cues exposed (e.g., sucrose or ammonia powder). A neural network based on state-of-the-art techniques (i.e., SLEAP) for pose estimation was built to identify the proximal and distal ends of the antennae. The network was optimised via grid search, resulting in a mean Average Precision (mAP) of 83.74%. To classify the stimulus type, another network was employed to take in a series of keypoint sequences, and output the stimulus classification. To find the best one-dimensional convolutional and recurrent neural networks, a genetic algorithm-based optimisation method was used. These networks were validated with iterated K-fold validation, obtaining an average accuracy of 45.33% for the former and 44% for the latter. Notably, we published and introduced the first dataset on cricket recordings that relate this animal’s behaviour to chemical stimuli. Overall, this study proposes a novel and simple automated method that can be extended to other animals for the creation of Biohybrid Intelligent Sensing Systems (e.g., automated video-analysis of an organism’s behaviour) to be exploited in various ecological scenarios.

Multi-objective niching quantum genetic algorithm-based optimization method for pneumatic hammer structure

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in various drilling applications owing to its high drilling efficiency, low gas consumption, non-polluting nature, and safety. Optimizing the hammer structure using empirical approaches is challenging due to the highly complex nonlinear problem caused by the piston movement. To address it, the intelligent algorithm is introduced for the first time and a multi-objective niching quantum genetic algorithm-based (MNQGA) optimization method for pneumatic hammer structure design is proposed in this study, to simultaneously maximize impact energy and minimize air consumption or optimize these objectives individually. Additionally, a novel niching strategy, corresponding adaptive quantum rotation gate, and mutation strategy are designed for the proposed method. The radial structures of a GQ127-type pneumatic hammer are optimized to maximize impact energy, followed by the optimization of the axial structures to satisfy four working conditions. The results show that different optimization objectives and constraints lead to different axial structural parameter values. After optimization, the hammer air consumption is reduced by at least 50.80 % at a constant impact energy of 350 J. The hammer impact energy is improved by more than 15.93 % if the volumetric flow rate of the supplied air is maintained at 10 m3/min. In addition, the effects of the hammer’s axial structure on its impact energy are investigated in detail based on an analytical model to validate the results of the proposed method. Five axial structure parameters are analyzed that have different effects on the impact energy. Within the given constraints, these parameters have their own optimal values that can be easily obtained using the proposed method. That is, the optimizations using the proposed method are consistent with those of the analytical models published in the literature. Therefore, the proposed method is efficient and accurate and can be used confidently when designing pneumatic hammers.

A Hybrid Genetic Algorithm-Based Parameter Identification Method for Nonlinear Hysteretic System with Experimental Verification

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method.

Neural network structure optimization for Hefei Light Source II β function correction

In this study, we address the design challenges related to hyperparameters, such as the number of layers and nodes in deep neural networks, by introducing an Improved Genetic Algorithm-based method for optimizing neural network structures (IGA-DNN). We apply this method to the practical problem of β function correction in particle accelerators and develop a storage ring β function correction scheme based on IGA-DNN. We compare our approach with traditional genetic algorithm-optimized neural networks to evaluate its performance. Our results reveal that the neural network optimized by the improved genetic algorithm reduces the number of layers by three and decreases training time by a factor of three, leading to a more efficient model. Moreover, the accuracy of β function simulation correction is enhanced using the IGA-DNN method. This approach can also be extended to optimize other optical parameters and tackle multi-parameter optimization problems, showcasing its versatility and potential for broader applications across various fields.

A Genetic Algorithm-based sequential instance selection framework for ensemble learning

The accumulation of large amounts of historical data has led to the wide application of ensemble learning over the past few decades, but the balance between the individual accuracy of base classifiers (BCs) and the diversity among these BCs is rarely considered in the construction of ensemble models. Since such a balance is crucial to the success of ensemble models, this paper proposes a Genetic Algorithm-based sequential instance selection framework to address this research gap. The novelties of the proposed framework include: transforming the balance between the individual accuracy of BCs and the diversity among BCs into a general combinatorial optimization model and designing a Genetic Algorithm-based evolutionary instance selection method to solve this model. The proposed framework not only overcomes the inherent limitations of the Genetic Algorithm in some high-dimensional tasks but also provides an explicit and automatic way to balance the accuracy and diversity by searching appropriate training data subsets for different component BCs. Based on obtained training data subsets, the component BCs of the ensemble model are generated sequentially, and their predictions are further combined with the weighted majority voting rule. Using 30 real datasets collected from various practical applications, such as medicine, business, and industry, the effectiveness of the proposed framework in constructing powerful ensemble models is examined and compared with six benchmark ensemble learning methods. In addition, the capability of the proposed framework to improve convergence performances is also examined by the comparison with the traditional Genetic Algorithm.