Abstract
This paper evaluates the effectiveness of using gene regulatory networks to manage gene expression in evolutionary algorithms for the purpose of balancing exploitation versus exploration. This builds on previous work that has shown that the introduction of non-coding genes can improve the ability of an evolutionary algorithm to adapt to change in the environment. As part of the paper an algorithm is developed and a prototype is implemented. The developed algorithm is compared to the standard genetic algorithm and previously developed methods for managing gene expression. Results show that the developed algorithm can outperform the standard genetic algorithm in dynamic environments. The algorithm is however not able to outperform all the other developed methods of managing gene expression and avenues for future improvement will be explored.
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Cilliers, M., Coulter, D.A. (2021). Managing Gene Expression in Evolutionary Algorithms with Gene Regulatory Networks. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_7
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DOI: https://doi.org/10.1007/978-3-030-86271-8_7
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