Extending Genetic Algorithms with Biological Life-Cycle Dynamics.

Autor: Felix-Saul JC; Division of Graduate Studies and Research, Tijuana Institute of Technology, Tecnológico Nacional de México (TecNM), Tijuana 22414, Mexico., García-Valdez M; Division of Graduate Studies and Research, Tijuana Institute of Technology, Tecnológico Nacional de México (TecNM), Tijuana 22414, Mexico., Merelo Guervós JJ; Department of Computer Engineering, Automatics and Robotics, University of Granada, 18071 Granada, Spain., Castillo O; Division of Graduate Studies and Research, Tijuana Institute of Technology, Tecnológico Nacional de México (TecNM), Tijuana 22414, Mexico.
Jazyk: angličtina
Zdroj: Biomimetics (Basel, Switzerland) [Biomimetics (Basel)] 2024 Aug 06; Vol. 9 (8). Date of Electronic Publication: 2024 Aug 06.
DOI: 10.3390/biomimetics9080476
Abstrakt: In this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth, reproduction, and death into the algorithm's framework. We consider an asynchronous execution of life cycle stages to individuals in the population, ensuring a steady-state evolution that preserves high-quality solutions while maintaining diversity. Experimental results demonstrate that the proposed extension outperforms traditional GAs and is as good or better than other well-known and well established algorithms like PSO and EvoSpace in various benchmark problems, particularly regarding convergence speed and solution qu/ality. The study concludes that incorporating biological life-cycle dynamics into GAs enhances their robustness and efficiency, offering a promising direction for future research in evolutionary computation.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje