Biomimetics (Basel)
Division of Graduate Studies and Research, Tijuana Institute of Technology, Tecnológico Nacional de México (TecNM), Tijuana 22414, Mexico.
Published: August 2024
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.
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http://dx.doi.org/10.3390/biomimetics9080476 | DOI Listing |
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Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore.
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Division of Blood Disorders and Public Health Genomics, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
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Department of Forensic Medicine, School of Basic Medical Sciences, Central South University, No172. Tongzipo Road, Changsha, Hunan 410013, People's Republic of China.
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