Extending Genetic Algorithms with Biological Life-Cycle Dynamics Felix Saul, J. C. García-Valdez, Mario Merelo Guervos, Juan Julián Castillo, Óscar Evolutionary algorithms Genetic algorithms Computational optimization 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. 2024-09-18T08:16:54Z 2024-09-18T08:16:54Z 2024-08-06 journal article Felix-Saul, J.C.; Garcia-Valdez, M.; Merelo Guervós, J.J.; Castillo, O. Extending Genetic Algorithms with Biological Life-Cycle Dynamics. Biomimetics 2024, 9, 476. https://doi.org/10.3390/biomimetics9080476 https://hdl.handle.net/10481/94627 10.3390/biomimetics9080476 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI