On the Search for Supersingular Elliptic Curves and Their Applications by Ismel Martinez-Diaz, Rashad Ali, Muhammad Kamran Jamil

“…As our main result, we define for the first time an objective function to measure the supersingularity in ordinary curves, and we apply local search and a genetic algorithm using that function. The study not only finds these supersingular elliptic curves but also investigates possible uses for them. …”
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Parametric Evaluation of Simultaneous Effects of Damaged Zone Parameters and Rock Strength Properties on GRC by Ali Ghorbani, Hadi Hasanzadehshooiili, Amin Eslami

“…Besides, effects of intermediate principal stress and the exponential decaying dilation parameter are taken into account thanks to adoption of the unified strength criterion (USC) as the material strength criteria. To do so, genetic algorithm (GA) via the method of evolutionary polynomial regression (EPR) is used to find a relationship between a number of 19 affecting parameters on the GRC as the input, and the internal support pressure as the target of prediction. …”
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Optimal Sizing of a Hybrid Renewable Photovoltaic-Wind System-Based Microgrid Using Harris Hawk Optimizer by Abdullrahman A. Al-Shamma’a, Hassan M. Hussein Farh, Abdullah M. Noman, Abdullah M. Al-Shaalan, Abdulaziz Alkuhayli

“…The efficacy of HHO is investigated, and its performance was compared with seven metaheuristic techniques, grasshopper optimization algorithm (GOA), cuckoo search optimizer (CSO), genetic algorithm (GA), Big Bang–Big Crunch (BBBC), coyote optimizer, crow search, and butterfly optimization algorithm (BOA), to attain the HRE microgrid optimal sizing based on annualized system cost (ASC) reduction. …”
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Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis by Mamidipaka Hema, Jami Venkata Suman, Boddepalli Kiran Kumar, Adisu Haile

“…Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. …”
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Crashworthiness Design and Multiobjective Optimization for Hexagon Honeycomb Structure with Functionally Graded Thickness by Ruixian Qin, Junxian Zhou, Bingzhi Chen

“…In addition, three surrogate models, including radial basis function (RBF), response surface method (RSM), and kriging (KRG), are compared in the accuracy of predicting SEA and PCF and capacity for optimization design of FGT honeycomb structure; the Nondominated Sorting Genetic Algorithm (NSGA-II) is applied to obtain the Pareto optimal solutions for the maximum thickness, minimum thickness, and thickness variation gradient exponent of a honeycomb wall. …”
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Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model by Jinman Zhang, Liangji Xu, Jiewei Li, Yueguan Yan, Ruirui Xu

“…To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. …”
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A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019 by Miao Fang

“…This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. …”
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Photovoltaic Generation Integration with Radial Feeders Using GA and GIS by Elias Mandefro Getie, Belachew Bantyirga Gessesse, Tewodros Gera Workneh

“…The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. …”
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Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction by Xiaojie Huang, Jiajia Chen, Peineng Liu

“…The balanced random forest classifier, optimized using the genetic algorithm for feature selection, achieved an area under the receiver operating characteristic curve (AUC) of 0.8708 and an accuracy of 82.67 % on the internal test set, with an accuracy of 86.36 % on the external validation set. …”
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RSM-, ANN-, and GA-Based Process Optimization for Acid Centrifugation Treatment of Cane Molasses Toward Mitigating Calcium Oxide Fouling in Ethanol Plant Heat Exchanger by Lata Deso Abo, Sintayehu Mekuria Hailegiorgis, Mani Jayakumar, Sundramurthy Venkatesa Prabhu, Gadissa Tokuma Gindaba, Abas Siraj Hamda, B. S. Naveen Prasad

“…Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R-squared value of 0.99866. However, the genetic algorithm (GA) optimization resulted in an actual CaO reduction of 66.21 wt.% with a t-test value of 0.497726.…”
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Research on Optimization of Injection Molding Process Parameters of Automobile Plastic Front-End Frame by Kai Yang, Lingfeng Tang, Peng Wu

“…A back propagation (BP) neural network model with input as a process parameter and output as an evaluation index is established by MATLAB and optimized by a genetic algorithm (GA). Finally, the optimized neural network model was used to predict the combination of process parameters with the minimum volume shrinkage and warpage amount. …”
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Optimal Pricing Strategy of Electric Vehicle Charging Station for Promoting Green Behavior Based on Time and Space Dimensions by Xiaomin Xu, Dongxiao Niu, Yan Li, Lijie Sun

“…Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. …”
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Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors by Wentong Hu, Donghao Miao, Chi Zhang, Zixian He, Wenquan Gu, Dongguo Shao

“…The best IOA-ML model for total phosphorus (TP), total nitrogen (TN), and permanganate index (CODMn) was extreme gradient boosting optimized by genetic algorithm (GA-XGB), while that for dissolved oxygen (DO) and turbidity was categorical boosting regression optimized by GA (GA-CBR). …”
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Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in Nort... by Xiuyu Liu, Zhen Zhang, Tao Jiang, Xuehua Li, Yanyi Li

“…Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. …”
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Optimization of Irrigation and Fertilization in Maize–Soybean System Based on Coupled Water–Carbon–Nitrogen Interactions by Aizheng Yang, Shuyuan Luo, Yaowen Xu, Pingan Zhang, Zhenyi Sun, Kun Hu, Mo Li

“…A multi-objective optimization model, integrating experimental data and mechanistic insights, was constructed and refined using the NSGA-III genetic algorithm to identify the optimal water and nitrogen application ratios. …”
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Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network by Wenguang Yu, Guofeng Guan, Jingchao Li, Qi Wang, Xiaohan Xie, Yu Zhang, Yujuan Huang, Xinliang Yu, Chaoran Cui

“…This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. …”
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Hot deformation physical mechanisms and a unified constitutive model of a solid solution Ti55511 alloy deformed in the two-phase region by Huijie Zhang, Y.C. Lin, Gang Su, Yongfu Xie, Wei Qiu, Ningfu Zeng, Song Zhang, Guicheng Wu

“…Material constants are determined using a genetic algorithm (GA), and the experimental data align well with the predicted data. …”
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Application of Extreme Gradient Boosting Based on Grey Relation Analysis for Prediction of Compressive Strength of Concrete by Liyun Cui, Peiyuan Chen, Liang Wang, Jin Li, Hao Ling

“…Another highlight is that its performance was compared with the frequently used artificial neural network (ANN) and genetic algorithm-artificial neural network (GA-ANN) by using random dataset and the same testing datasets. …”
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Research on Simulation and Performance Optimization of Mach 4 Civil Aircraft Propulsion Concept by Min Chen, Zihao Jia, Hailong Tang, Yi Xiao, Yonghang Yang, Feijia Yin

“…Finally, a multiobjective optimization tool made up of a response surface method and a genetic algorithm was developed to optimize the design parameters and the control law of the TBCC engine, in order to make the Mach 4 supersonic civil aircraft engine with better performance, lower noise, and lower emissions. …”
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Multi-objective optimization of SUS430C steel turning process using hybrid machine learning and evolutionary algorithm approach by Nguyen Van-Canh, Nguyen Anh-Thang, Pham Ngoc-Linh, Nguyen Thuy-Duong

“…A hybrid approach combining Extreme Gradient Boosting (XGBoost) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was employed to optimize three critical machining objectives: surface roughness (Ra), material removal rate (MRR), and tool wear (Vb). …”
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