An RNA Evolutionary Algorithm Based on Gradient Descent for Function Optimization DOI Creative Commons
Qiuxuan Wu,

Zikai Zhao,

Mingming Chen

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(4), С. 332 - 357

Опубликована: Июль 3, 2024

Abstract The optimization of numerical functions with multiple independent variables was a significant challenge numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits function optimization, including rapid convergence, they have low accuracy can easily become trapped local optima. To address these issues, new heuristic algorithm proposed, gradient descent-based algorithm. Specifically, adaptive moment estimation (Adam) employed as mutation operator to improve the development ability Additionally, two operators inspired by inner-loop structure molecules were introduced: an crossover operator. These enhance global exploration early stages evolution enable it escape from consists stages: pre-evolutionary stage that employs identify individuals vicinity optimal region post-evolutionary applies descent further solution’s quality. When compared current advanced for solving problems, Adam Genetic Algorithm (RNA-GA) produced better solutions. In comparison RNA-GA (GA) across 17 benchmark functions, ranked first best result average rank 1.58 according Friedman test. set 29 CEC2017 suite, such African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Grey Wolf Optimizer, 1.724 Our not only achieved improvements over but also performed excellently among various achieving high precision optimization.

Язык: Английский

Enhancing Cryptocurrency Price Prediction through Inter-Coin Volatility and Hyperparameter Optimization DOI

Nasreddine Hafidi,

Zakaria Khoudi, Mourad Nachaoui

и другие.

Computational Economics, Год журнала: 2025, Номер unknown

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

0
Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer DOI Creative Commons
Mansourah Aljohani, Yousry AbdulAzeem, Hossam Magdy Balaha

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(3), С. 308 - 325

Опубликована: Май 1, 2024

Abstract Feature selection (FS) is vital in improving the performance of machine learning (ML) algorithms. Despite its importance, identifying most important features remains challenging, highlighting need for advanced optimization techniques. In this study, we propose a novel hybrid feature ranking technique called Hybrid Ranking Weighted Majority Model (HFRWM2). HFRWM2 combines ML models with Harris Hawks Optimizer (HHO) metaheuristic. HHO known versatility addressing various challenges, thanks to ability handle continuous, discrete, and combinatorial problems. It achieves balance between exploration exploitation by mimicking cooperative hunting behavior Harris’s hawks, thus thoroughly exploring search space converging toward optimal solutions. Our approach operates two phases. First, an odd number models, conjunction HHO, generate encodings along metrics. These are then weighted based on their metrics vertically aggregated. This process produces rankings, facilitating extraction top-K features. The motivation behind our research 2-fold: enhance precision algorithms through optimized FS improve overall efficiency predictive models. To evaluate effectiveness HFRWM2, conducted rigorous tests datasets: “Australian” “Fertility.” findings demonstrate navigating We compared 12 other techniques found it outperform them. superiority was particularly evident graphical comparison dataset, where showed significant advancements ranking.

Язык: Английский

Процитировано

2
An RNA Evolutionary Algorithm Based on Gradient Descent for Function Optimization DOI Creative Commons
Qiuxuan Wu,

Zikai Zhao,

Mingming Chen

и другие.

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(4), С. 332 - 357

Опубликована: Июль 3, 2024

Abstract The optimization of numerical functions with multiple independent variables was a significant challenge numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits function optimization, including rapid convergence, they have low accuracy can easily become trapped local optima. To address these issues, new heuristic algorithm proposed, gradient descent-based algorithm. Specifically, adaptive moment estimation (Adam) employed as mutation operator to improve the development ability Additionally, two operators inspired by inner-loop structure molecules were introduced: an crossover operator. These enhance global exploration early stages evolution enable it escape from consists stages: pre-evolutionary stage that employs identify individuals vicinity optimal region post-evolutionary applies descent further solution’s quality. When compared current advanced for solving problems, Adam Genetic Algorithm (RNA-GA) produced better solutions. In comparison RNA-GA (GA) across 17 benchmark functions, ranked first best result average rank 1.58 according Friedman test. set 29 CEC2017 suite, such African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Grey Wolf Optimizer, 1.724 Our not only achieved improvements over but also performed excellently among various achieving high precision optimization.

Язык: Английский

Процитировано

0