Vol. 3 No. 1 (2023): Hong Kong Journal of AI and Medicine
Articles

Hybrid Evolutionary Algorithms - Integration Strategies

Dr. Kenji Tanaka
Professor, AI in Medicine, Sakura University, Tokyo, Japan
Cover

Published 17-04-2023

Keywords

  • Hybrid Evolutionary Algorithms,
  • Integration Strategies,
  • Optimization, Combinatorial Optimization,
  • Metaheuristics,
  • Evolutionary Computation

How to Cite

[1]
Dr. Kenji Tanaka, “Hybrid Evolutionary Algorithms - Integration Strategies”, Hong Kong J. of AI and Med., vol. 3, no. 1, pp. 40–50, Apr. 2023, Accessed: Sep. 19, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/7

Abstract

Hybrid Evolutionary Algorithms (HEAs) have emerged as powerful optimization techniques that combine the strengths of different algorithms to tackle complex optimization problems. This paper presents a comprehensive review of integration strategies used in HEAs, focusing on how various algorithms are combined to improve search efficiency and solution quality. We discuss the motivations behind hybridization, categorize integration strategies based on their characteristics, and highlight key advancements in the field. Additionally, we provide insights into the design and implementation of HEAs, discussing challenges and future research directions.

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References

  1. 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.
  2. Pillai, Aravind Sasidharan. "Advancements in Natural Language Processing for Automotive Virtual Assistants Enhancing User Experience and Safety." Journal of Computational Intelligence and Robotics 3.1 (2023): 27-36.
  3. Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.