Evolutionary Genetic Algorithm Research Articles

Intelligent Indoor Layout Design Based on Interactive Genetic and Differential Evolution Algorithms

Intelligent Indoor Layout Design Based on Interactive Genetic and Differential Evolution Algorithms

Modeling thermoelectric performance of doped BiCuSeO oxychalcogenide ceramics using genetically hybridized support vector regression computational method

Oxychalcogenide BiCuSeO thermoelectric material has demonstrated significant potentials in addressing energy crisis through thermal to electrical energy conversion. Low toxicity, reduced cost of input materials and good stability when operated at high temperatures are unique features that promote the candidature of BiCuSeO ceramics. However, the thermoelectric figure of merit (FM) which influences the conversion (energy) efficiency of BiCuSeO material is characterized with small value due to low carrier mobility and concentration. Incorporation of various kind of dopants at different sites, vacancies and defects creation are among the experimental approaches of enhancing thermoelectric performance (usually, figure of merit). In order to circumvent laborious experimental procedures and to strengthen material design for sustainable applications, the maximum value of thermoelectric figure of merit of doped BiCuSeO ceramics is modeled in this work through genetic evolutionary algorithm hybridized support vector regression (HSVR) using ionic radii and dopant concentrations-based descriptors. FM-HSVR-G model developed with Gaussian kernel performs better than FM-HSVR-P that employs polynomial transformation kernel using different performance assessment parameters such as root mean square error (RMSE), correlation coefficient (CC) and mean absolute error (MAE). When validated on the testing samples of BiCuSeO ceramics, FM-HSVR-G model shows 45.40 % (for CC parameter), 16.87 % (for MAE parameter) and 14.00 % (for RMSE parameter) performance improvement over FM-HSVR-P model. Comparison of the present models with the existing machine learning (ML) model indicates that FM-HSVR-P and FM-HSVR-G model outperform ML (2022) model with enhancement of 22.05% and 72.04 %, respectively using metric based on RMSE. Ability of some elemental dopants to strengthen the thermoelectric performance of pristine BiCuSeO ceramic was investigated using the model developed while the obtained behavior agrees well with the experimental trends. The characteristic precision and accuracy of the developed models would definitely strengthen thermoelectric performance of BiCuSeO based compounds and further facilitate BiCuSeO ceramic material design for controlling energy crisis and other sustainable applications.

Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization

Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used.

Optimization of a Circular Planar Spiral Wireless Power Transfer Coil Using a Genetic Algorithm

Optimization of a Circular Planar Spiral Wireless Power Transfer Coil Using a Genetic Algorithm

Heuristic control of power consumption by up to 1000 V electrical loads at mining enterprises

Purpose. To develop a method for synthesizing the structure and algorithm of the system for automated control of power consumption by up to 1000 V electrical receivers at mining enterprises with iron ore underground mining methods. This enables direct control of the load connection to the industrial power grid to ensure minimum power costs depending on its cost per day ahead. Methodology. The problem of controlling power consumption of electrical receivers at iron ore underground mines is formalized as a binary form of mixed integer programming. To solve it, a binary implementation of the heuristic genetic algorithm is used. The mathematical modeling method analyzes the impact of genetic algorithm settings, such as the number of phenotypes in the population, the number of elite phenotypes that pass unchanged to the next generation, and the method of phenotype crossover on its quality. Findings. As a result of the research, it is found that the most effective way to control the process of power consumption based on an evolutionary genetic algorithm is to use the Laplace crossover function and keep the percentage of elite phenotypes in the population at 10 %. Moreover, at the smallest population size, the best accuracy is observed when using the Laplace function, while at one- and two-point crossover functions, it worsens, but not significantly (no more than 0.2 %). However, as the number of elite phenotypes increases, the duration of the evolutionary search in the control process is reduced by almost a factor of two in the case of one- and two-point crossovers. Originality. For the first time, the structure of a heuristic system for automated control of power consumption by underground electrical receivers with a supply voltage of up to 1000 V at iron ore underground mines has been developed on the basis of an evolutionary genetic algorithm. Depending on the designed volumes of ore production and the daily power cost per day, this allows determining the optimal power load schedule of underground distribution substations in advance, which, subject to the accepted limits on hourly and daily power, minimizes the cost of purchasing power, and thus reduces the cost of the final product. Practical value. The architecture of a heuristic system for controlling power consumption by electrical receivers with a voltage of up to 1000 V based on an evolutionary genetic algorithm is developed and recommended when optimizing the power load schedule of transformer substations of mining and metallurgical enterprises, in particular, of iron ore underground mines operating in this voltage class.

4E analysis and triple objective NSGA-II optimization of a novel solar-driven combined ejector-enhanced power and two-stage cooling (EORC-TCRC) system

This study proposed an innovative combined ejector-enhanced organic Rankine cycle and two-stage compression refrigeration cycle (EORC-TCRC), to investigate its potential to revolutionize energy utilization and offer a sustainable solution for the current energy challenge. Energy, exergy, economic, and environmental (4E) analysis of the novel EORC-TCRC system was conducted first. The performance appraisal of the novel system compared to the conventional combined power and ejector refrigeration system has been evaluated. The evolutionary non-dominated Sort Genetic (NSGA-II) optimization algorithm was implemented to ascertain triple-objective optimal system operating conditions. The results revealed a significant improvement in refrigeration output, energy, and exergy efficiency with values of 220.06 kW, 11.67%, and 17.07%, respectively, compared to the conventional Rankine power and ejector refrigeration system. By different selections of the objective functions, four groups comprised of Multi-Objective CT–ղex, Multi-Objective CT–ղTh, Multi-Objective ղex–ղTh, and Triple-Objective mode presented to sought NSGA-II optimization results. The optimization results of Multi-Objective CT–ղTh mode indicated that the best thermal efficiency and overall system cost rate operating conditions are 28.25% and 78,820 ($/year), respectively. While the optimal system operating condition occurs in the Triple-Objective ղex- ղTh-CT with the exergetic efficiency of 41.69%.

NEMA NU 2-2018 evaluation and image quality optimization of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT using a genetic evolutionary algorithm

A performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32 cm extended field of view. The first commercially available clinical digital bismuth germanate system. The system does not use time of flight (ToF). Testing was performed in accordance with the NEMA NU2–2018 standard. A comparison was made between two other commercial GE scanners with extended fields of view; the Discovery MI − 6 ring (ToF enabled) and the Discovery IQ (non-ToF). A genetic evolutionary algorithm was developed to optimize image reconstruction parameters from image quality assessments. The Omni demonstrated average spatial resolutions at 1 cm radial offset as 3.9 mm FWHM. The total system sensitivity at the center was 44.36 cps/kBq. The peak NECR was measured as 501 kcps at 17.8 kBq ml−1 with a 35.48% scatter fraction. The maximum count-rate error below NECR peak was 5.5%. Using standard iterative reconstructions, sphere contrast recovery coefficients were from 52.7 ± 3.2% (10 mm) to 92.5 ± 2.4% (37 mm). The PET-CT co-registration accuracy was 2.4 mm. In place of ToF, the Omni employs software corrections through a pre-trained neural network (PDL) (trained on non-ToF to ToF) that takes Bayesian penalized likelihood reconstruction (Q.Clear) images as input. The optimum parameters for image reconstruction, determined using the genetic algorithm were a Q.Clear parameter, β, of 350 and a ‘medium’ PDL setting. Using standard iterative reconstructions, the Omni initially showed increased background variability compared to the Discovery MI. With optimized PDL reconstruction parameters selected using the genetic algorithm the performance of the Omni surpassed that of the Discovery MI on all NEMA tests. The genetic algorithm’s demonstrated ability to enhance image quality in PET-CT imaging underscores the importance of algorithm driven optimization and underscores the requirement to validate its use in the clinical setting.

Genetic Algorithm-Based Optimisation of a Double-Wall Effusion Cooling System for a High-Pressure Turbine Nozzle Guide Vane

Double-Wall Effusion Cooling schemes present an opportunity for aeroengine designers to achieve high overall cooling effectiveness and convective cooling efficiency in High-Pressure Turbine blades with reduced coolant usage compared to conventional cooling technologies. This is accomplished by combining impingement, pin-fin and effusion cooling. Optimising these cooling schemes is crucial to ensuring that cooling is achieved sufficiently at high-heat-flux regions and not overused at low-heat-flux ones. Due to the high number of design variables employed in these systems, optimisation through the use of Computational Fluid Dynamics (CFD) simulations can be a computationally costly and time-consuming process. This study makes use of a Low-Order Flow Network Model (LOM), developed, validated and presented previously, which quickly assesses the pressure, temperature, mass flow and heat flow distributions through a Double-Wall Effusion Cooling scheme. Results generated by the LOM are used to rapidly produce an ideal cooling system design through the use of an Evolutionary Genetic Algorithm (GA) optimisation process. The objective is to minimise the coolant mass flow whilst maintaining acceptable metal cooling effectiveness around the external surface of the blade and ensuring that the Backflow Margin for all film holes is above a selected threshold. For comparison, a Genetic Aggregation model-based optimisation using CFD simulations in ANSYS Workbench is also conducted. Results for both the reduction of coolant mass flow and the total optimisation runtime are analysed alongside those from the LOM, demonstrating the benefit of rapid low-order solving techniques.

Superconductivity of cubic MB6 (M = Na, K, Rb, Cs).

Previous studies have shown that NaB6, KB6, and RbB6 adopting Pm3̄m are superconductors with a relatively high Tc under ambient conditions. In this paper, we conducted systematic structural and related properties research on CsB6 through a genetic evolution algorithm and total energy calculations based on density functional theory between 0 and 20GPa. Our results reveal a cubic Pm3̄m CsB6, which is dynamically stable under the pressures we studied. We systematically calculated the formation enthalpies, electronic properties, and superconducting properties of Pm3̄m MB6 (M = Na, K, Rb, Cs). They all exhibit metallic features, and boron has high contributions to band structures, density of states, and electron-phonon coupling (EPC). The calculated results about the Helmholtz free energy difference of Pm3̄m CsB6 at 0, 10, and 20GPa indicate that it is stable upon chemical decomposition (decomposition to simple substances Cs and B) from 0 to 400K. The phonon density of states indicates that boron atoms occupy the high frequency area. The EPC results show that Pm3̄m CsB6 is a superconductor with Tc = 11.7K at 0GPa, close to NaB6 (13.1K), KB6 (11.7K), and RbB6 (11.3K) at 0GPa in our work, which indicates that boron atoms play an essential role in superconductivity: vibrations of B6 regular octagons lead to the high Tc of Pm3̄m MB6. Our work about Pm3̄m hexaborides provides a supplementary study on the borides of the group IA elements (without Fr and Li) and has an important guiding significance for the experimental synthesis of CsB6.

Energy efficient design of regenerative shock absorbers for automotive suspensions: A multi-objective optimization framework

This study addresses the optimized design of electro-hydrostatic regenerative shock absorbers to enhance vibrational energy recovery in ground vehicles, aiming to reduce carbon footprint. The design strategy focuses on maximizing regeneration efficiency while minimizing actuator volume. Important trade-offs are considered as constraints, such as ride comfort and road holding. The approach employs a multi-objective evolutionary genetic algorithm, validated through numerical analysis, and applied to design a prototype. Experimental results show a peak regeneration efficiency of 45%, and simulations on a class-B vehicle indicate an average regenerated power of 101W per shock absorber, corresponding to a CO2 emission reduction of 5.25g/km.

Multimethod computing technology for searching energy-optimal motion modes of electric rolling stock of railways

The article is devoted to the problem of finding energy-optimal modes of movement of electric rolling stock of railways, considered as a set of problems of finding a global extremum of a functional defined on a set of logical-dynamic system (LDS) movement trajectories that are acceptable under operating conditions, and a structural-parametric synthesis of the LDS control strategy and optimization of its parameters based on random search methods, swarm intelligence, evolutionary algorithms and multi-agent models. A multi-method technology for optimizing the modes of train movement according to the criterion of minimum energy consumption, and software and algorithmic support based on the original multi-agent evolutionary-swarm method for synthesizing the optimal-terminal control of multi-mode moving objects developed by the author are proposed. The specified computing technology includes the following stages: formation of a set of promising control strategies; their optimization and recombination based on the evolutionary genetic algorithm; finding the global extremum of the functional using parametric optimization of the solutions identified at the previous stages using the modified particle swarm method. The computational experiments performed with the simulation model of train movement showed that when a sufficiently large number of options for control strategies are generated at the first stage, the algorithm converges to the global extremum of the functional.

Integrated Decision Making to Determine the Optimal Order Quantity for Raw Materials Using Genetic Evolutionary Algorithms

Decision Making in economic organizations, especially the productivity, is one of the problems that researchers are interested in. It is important for the organizations that seek global competition and the integration of their decision will lead to coordination of decisions between the departments of these organizations to determine optimal order quantity and establish a correct inventory policy to warrant production with least total costs (costs of transportation, holding and purchasing), these is the objective of any company to achieve an adequate and enough inventory level to meet the future needs. In this paper, integrated decision making with three stages, first stage is forecasting with demands to final products using time series, second stage determine required quantities for raw material to manufacture these final products, and formulated a mathematical model reduces the total cost in third stage, when the transportation and purchase are variable costs with order quantity, while in just-in-time model or economic order quantity model are a fixed demand and purchase cost. The aim of this paper is integrated decision making to reduce total cost of required raw materials for the manufacturing processes and without and determine the optimal order quantity using genetic algorithms. This study was applied in Wasit company for cotton products in the textile factory, the results shown Holt-Winter method is the best method to forecasting because has least mean absolute error and the percentage of purchasing cost 73%, Transportation cost 8%, and holding cost 19%. The percentage of purchasing cost of cotton is biggest value, more 99% of purchasing cost.

Microscopic urinary particle detection by different YOLOv5 models with evolutionary genetic algorithm based hyperparameter optimization

The diagnosis of kidney disease often involves analysing urine sediment particles. Traditionally, urinalysis was performed manually by collecting urine samples and using a centrifuge, which was prone to manual errors and relied on labour-intensive processes. Automated urine sediment microscopy, based on machine learning models, requires segmentation and feature extraction, which can hinder model performance due to intrinsic characteristics of microscopic images. Deep learning models based on convolutional neural networks (CNNs) often rely on a large number of manually annotated data, making the system computationally complex. This study propose an advanced deep learning model based on YOLOv5, which offers faster performance and requires comparatively less data. The proposed model used five variants of the YOLOv5 model (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) to detect six categories of urine particles (erythrocyte, leukocyte, crystals, cast, mycete, epithelial cells) from microscopic urine sediment images. The dataset involved 5376 images of urine sediments with 6 particles. There are 30 sets of hyperparamreteres are employed in the YOLOv5 model. To optimize the hyperparameters and fine-tune with the urine sediment dataset and for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among the six categories of detected particles mycete achieved maximum performance with a mAP of 97.6 % and crystals achieved minimum performance with a mAP of 81.7 % with YOLOv5x model compared to other particles. To optimize the hyperparameters for training each model, the system employed a genetic algorithm (GA) based on evolutionary principles named as Evolutionary Genetic Algorithm (EGA). Among all the models, YOLOv5l and YOLOv5x performed the best. YOLOv5l achieved a mean average precision (mAP) of 85.8 % while YOLOv5x achieved a mAP of 85.4 % at an IoU threshold of 0.5. The detection speed per image was 23.4 ms for YOLOv5l and 28.4 ms for YOLOv5x. The proposed method developed a faster and better automated microscopic model using advanced deep learning techniques to detect urinary particles from microscopic urine sediment images for kidney disease identification. The method demonstrated strong performance in urinalysis.

Achieving energy independence in urban microgrids: Strategies for domestic resource utilization and environmental sustainability

The quest for energy independence within urban microgrids (MGs) has become increasingly crucial for ensuring domestic resource utilization and environmental sustainability. One of the pivotal challenges lies in the clustering of MGs, a complex task aimed at enhancing their robustness and economic performance during events. However, limited research has been conducted on this intricate clustering problem. This study addresses this gap by proposing an artificial intelligence-based method tailored for MG clustering. In this paper, we introduce a novel multi-objective optimization approach to determine the optimal clustering of standalone MGs. Our methodology considers a range of critical parameters, including distributed generation (DG) droop settings, DG locations and capacities, renewable energy resources from solar units and wind turbines, capacitors, and power transmission lines. By minimizing power losses and enhancing voltage stability, we identify a virtual cut-set line, often referred to as the "virtual line," characterized by minimal power exchange between clustered MGs. To achieve optimal and cost-effective performance, we develop an enhanced differential genetic evolution algorithm designed to address multi-objective problems within stringent constraints. Additionally, we employ Bayesian theory within artificial intelligence to estimate the optimal output power of renewable energy sources, specifically wind turbines and solar panels. To validate the effectiveness of our approach, we apply it to a 69-bus MG across various scenarios. Our findings demonstrate the proficiency of our method in achieving energy independence, domestic resource utilization, and environmental sustainability within urban microgrids.

Reliability-based design optimization of tuned mass-damper-inerter for mitigating structural vibration

The tuned mass-damper-inerter (TMDI), comprising mass blocks, springs, dampers, and two-terminal inertial elements, has garnered significant attention as an advanced iteration of the tuned mass damper (TMD) in recent years. However, previous studies of TMDI's design are primarily conducted according to the theoretical formulas based on deterministic analysis assuming harmonic excitations or stochastic analysis assuming Gaussian white noise type excitations. This treatment falls short of capturing the non-stationary and non-Gaussian characteristics exhibited by engineering excitations and thus cannot ensure the safety of controlled structure. A few studies considering non-stationary excitations and based on reliability metrics, in conjunction with stochastic simulation methods, often confront excessive computational overhead. Aiming at such a challenge, the reliability-based design optimization (RBDO) of TMDI system, combining the probability density evolution method (PDEM) and genetic algorithm (GA), for mitigating structural vibration under non-stationary excitations is performed in this study. For the purposes of illustration, a single-degree-of-freedom system attached the TMDI and subjected to stochastic ground motions is studied. Considering the cases of different levels of inertance-to-mass ratios, the RBDO of the TMDI is carried out. The solutions by the theoretical formulas and by the variance-based design optimization are compared to verify the superiority of the proposed method. For assessing the seismic mitigation performance of the optimized TMDI system, probability density evolution analysis of structure-TMDI system with three levels of inertance-to-mass ratios is performed. Comparison between the RBDO of TMDI and that of TMD under consistent mass ratio is further addressed. Numerical results reveal the technical advantages of TMDI in a probabilistic sense, i.e., high vibration mitigation performance, considerable mass reduction, and less stroke demand.

Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms

In the complex landscape of production inventory management for manufacturing industries, this research brings to light the significant impact of imperfect items—often an overlooked aspect that can result in elevated costs and operational inefficiencies. Addressing this oversight, we introduce a tailored mathematical model that harmoniously integrates demand forecasting, production planning, quality checks, and inventory control measures, specifically when imperfect items are in play. Notably, our model leverages the power of hybrid intelligent algorithms, fusing fuzzy systems with both genetic and differential evolution (DE) algorithms. Through sensitivity analysis, we discern the intricate effects of varying parameters on system performance. To cater to different operational exigencies, we introduce two distinct decision-making approaches. Our results highlight a promising reduction in anticipated yearly expenses, bolstering the reliability of events and ensuring costs are kept within predefined financial thresholds. Furthermore, we unveil a pioneering hybrid DE algorithm that distinctly outperforms the foundational DE in optimization problem-solving. A side-by-side comparison of our introduced methods elucidates invaluable insights into inventory cash flow management. Practitioners stand to gain immensely from our findings, which pave the way for cost-effective strategies, superior quality control, and an uptick in overall operational efficiency in the face of imperfect items. This seminal work not only offers actionable strategies but also beckons future research opportunities, thereby pushing the frontiers of manufacturing systems.

Two-layer heuristic for the three-dimensional bin design and packing problem

The bin packing problem is a classical problem widely existing in practical applications, such as shipping, warehousing and manufacturing industries. Whereas current research mainly focuses on reducing packing costs by optimizing the packing scheme, this study explores a novel approach by redesigning the bin sizes to fit the items ready to be packed. Specifically, this study considers a general three-dimensional open dimension problem (3D-ODP) where all dimensions, i.e. length, width and height, of a number of heterogeneous bin types are unknown and need to be decided. Based on the designed bin types, the corresponding packing scheme with minimal total costs is optimized, which is referred to as a three-dimensional multiple-bin-size bin packing problem (3D-MBSBPP). The combination of 3D-ODP and 3D-MBSBPP is redefined as a three-dimensional bin design and packing problem (3D-BDPP), for which a two-layer heuristic is developed. It consists of an outer heuristic framework (i.e. genetic algorithm or differential evolution algorithm) to design bin types, and an inner deterministic constructive heuristic (i.e. placement algorithm) to generate an effective solution for the 3D-MBSBPP. Extensive simulation tests on the classical benchmark and a case study derived from an e-commerce company are conducted to verify the algorithms and deduce managerial insights.

Lessons from the Evolutionary Computation Bestiary.

The field of metaheuristics has a long history of finding inspiration in natural systems, starting from evolution strategies, genetic algorithms, and ant colony optimization in the second half of the 20th century. In the last decades, however, the field has experienced an explosion of metaphor-centered methods claiming to be inspired by increasingly absurd natural (and even supernatural) phenomena-several different types of birds, mammals, fish and invertebrates, soccer and volleyball, reincarnation, zombies, and gods. Although metaphors can be powerful inspiration tools, the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has been counterproductive to the scientific progress of the field, as it neither improves our ability to understand and simulate biological systems nor contributes generalizable knowledge or design principles for global optimization approaches. In this article we discuss some of the possible causes of this trend, its negative consequences for the field, and some efforts aimed at moving the area of metaheuristics toward a better balance between inspiration and scientific soundness.

Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate.

Toward an artificial intelligence-based decision framework for developing adaptive e-learning systems to impact learners’ emotions

ABSTRACT Learners’ emotional states might change during the learning process, and unpredictable variations of a person’s emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the learner’s emotional state through an evolutionary genetic algorithm to change learner’s mood and increase learning performance. This proposed framework was tested using a case study of English as a second language learner during one semester. The students were divided into two groups of participants (each group containing twenty students, forming a total of 40 students). The results of this study demonstrated the importance of learners’ emotions in their learning performance and proved the effectiveness of our proposed framework and the success of the recommended micro-break activities chosen based on learners’ emotions and preferences. The findings of this study have important practical implications in designing adaptive e-learning systems and learning management systems such as Moodle. They also contribute to theoretical implications in the field of AI and learner emotions by suggesting a novel approach to identifying, categorizing, and offering a learning path that can cater to the needs of individual learners.