Extending Genetic Algorithms with Biological Life-Cycle Dynamics

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 fr...

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Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Switzerland), 2024-08, Vol.9 (8), p.476
Main Authors: Felix-Saul, J C, García-Valdez, Mario, Merelo Guervós, Juan J, Castillo, Oscar
Format: Article
Language:English
Subjects:
Adaptability
Algorithms
Analysis
Artificial intelligence
bio-inspired algorithms
computational optimization
Evolution
evolutionary algorithms
Genetic algorithms
Genetic aspects
Health aspects
Life cycle assessment
Life cycles
Mathematical functions
Mutation
Optimization
Probability distribution
Swarm intelligence
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Summary: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.
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9080476