Abstract
Layered learning is a decomposition and reuse technique that has proved to be effective in the evolutionary solution of difficult problems. Although previous work has integrated it with genetic programming (GP), much of the application of that research has been in relation to multi-agent systems. In extending this work, we have applied it to more conventional GP problems, specifically those involving Boolean logic. We have identified two approaches which, unlike previous methods, do not require prior understanding of a problem’s functional decomposition into sub-goals. Experimentation indicates that although one of the two approaches offers little advantage, the other leads to solution-finding performance significantly surpassing that of both conventional GP systems and those which incorporate automatically defined functions.
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Jackson, D., Gibbons, A.P. (2007). Layered Learning in Boolean GP Problems. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_14
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DOI: https://doi.org/10.1007/978-3-540-71605-1_14
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