@misc{10481/94627, year = {2024}, month = {8}, url = {https://hdl.handle.net/10481/94627}, abstract = {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.}, organization = {TecNM grant number 21094.24-P}, organization = {Spanish Ministry of Science, Innovation and Universities MICIU/AEI/10.13039/501100011033 under project number PID2023-147409NB-C21}, organization = {DemocratAI PID2020-115570GB-C22}, publisher = {MDPI}, keywords = {Evolutionary algorithms}, keywords = {Genetic algorithms}, keywords = {Computational optimization}, title = {Extending Genetic Algorithms with Biological Life-Cycle Dynamics}, doi = {10.3390/biomimetics9080476}, author = {Felix Saul, J. C. and García-Valdez, Mario and Merelo Guervos, Juan Julián and Castillo, Óscar}, }