Vol. 3 No. 1 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Co-evolutionary Algorithms - Dynamics and Applications

Dr. Juan Ramirez
Lecturer, Health Data Analytics, Pacific University, Lima, Peru
Cover

Published 16-04-2023

Keywords

  • Co-evolutionary algorithms,
  • Evolutionary computation,
  • Fitness evaluation,
  • Selection mechanisms

How to Cite

[1]
Dr. Juan Ramirez, “Co-evolutionary Algorithms - Dynamics and Applications”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 1, pp. 16–22, Apr. 2023, Accessed: Nov. 13, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/7

Abstract

Co-evolutionary algorithms (CEAs) are a class of evolutionary algorithms where multiple populations evolve concurrently, influencing each other's evolution. This paper provides a comprehensive review of the dynamics and applications of CEAs, focusing on their ability to solve complex problems through the interaction of multiple populations. The paper discusses the underlying principles of CEAs, including fitness evaluation, selection mechanisms, and population dynamics. It also examines various application domains where CEAs have been successfully applied, such as optimization, game playing, and evolutionary robotics. The paper concludes with a discussion on the future directions and challenges in the field of co- evolutionary algorithms.

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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. 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.