Articles
Genetic Algorithm Optimization - Principles and Applications: Analyzing the principles and applications of genetic algorithms (GAs) for optimization tasks in various domains, including engineering, finance, and scheduling
Published 17-04-2023
Keywords
- Genetic algorithms,
- Optimization,
- Engineering,
- Finance,
- Scheduling
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
[1]
Prof. Jose Rodriguez, “Genetic Algorithm Optimization - Principles and Applications: Analyzing the principles and applications of genetic algorithms (GAs) for optimization tasks in various domains, including engineering, finance, and scheduling”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 28–38, Apr. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/3
Abstract
Genetic algorithms (GAs) are a class of optimization algorithms inspired by the principles of natural selection and genetics. They have been widely applied to solve complex optimization problems in various domains due to their effectiveness and flexibility. This paper provides a comprehensive analysis of the principles and applications of genetic algorithms, focusing on their use in engineering, finance, and scheduling.
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References
- Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
- Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
- Venigandla, Kamala, et al. "Leveraging AI-Enhanced Robotic Process Automation for Retail Pricing Optimization: A Comprehensive Analysis." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 361-370.
- Reddy, Surendranadha Reddy Byrapu. "Ethical Considerations in AI and Data Science-Addressing Bias, Privacy, and Fairness." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 1-12.
- Sasidharan Pillai, Aravind. “Utilizing Deep Learning in Medical Image Analysis for Enhanced Diagnostic Accuracy and Patient Care: Challenges, Opportunities, and Ethical Implications”. Journal of Deep Learning in Genomic Data Analysis 1.1 (2021): 1-17.
- Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.