Abstract
In this work we propose two hybrid algorithms combining evolutionary search with optimization algorithms. One algorithm memetically combines global evolution with gradient descent local search, while the other is a two-step procedure combining linear optimization with evolutionary search. It is shown that these algorithms typically produce smaller local unit networks with performance similar to theoretically sound but large regularization networks.
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Neruda, R., Vidnerová, P. (2010). Memetic Evolutionary Learning for Local Unit Networks. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_68
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DOI: https://doi.org/10.1007/978-3-642-13278-0_68
Publisher Name: Springer, Berlin, Heidelberg
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