Abstract
This paper proposes a two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed for finding an optimal solution.
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Xie, H. (2005). Diversity Control in GP with ADF for Regression Tasks. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_181
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DOI: https://doi.org/10.1007/11589990_181
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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