Too busy to learn

“Too busy to learn” by Francisco B. Pereira, Penousal Machado, Ernesto Costa, Amlcar Cardoso, Alberto Ochoa, Roberto Santana, and Marta Rosa Soto. In Proceedings of the 2000 Congress on Evolutionary Computation CEC-2000, (La Jolla Marriott Hotel La Jolla, California, USA), July 2000, pp. 720-727.

Abstract

The goal of this research is to analyze how individual learning helps an evolutionary algorithm in its search for best candidates for the Busy Beaver problem. To study this interaction two learning models, implemented as local search procedures, are proposed. Experimental results show that, in highly irregular and very prone to premature convergence search spaces, local search methods are not an effective help to evolution. In addition, one interesting effect related to learning is reported. When the mutation rate is too high, learning acts as a repair, reintroducing some useful information that was lost

BibTeX entry:

@inproceedings{Pereira_et_al:2000,
   author = {Francisco B. Pereira and Penousal Machado and Ernesto Costa
	and Amlcar Cardoso and Alberto Ochoa and Roberto Santana and Marta
	Rosa Soto},
   title = {Too busy to learn},
   booktitle = {Proceedings of the 2000 Congress on Evolutionary
	Computation CEC-2000},
   pages = {720--727},
   publisher = {IEEE Press},
   address = {La Jolla Marriott Hotel La Jolla, California, USA},
   month = jul,
   year = {2000},
   isbn = {0-7803-6375-2}
}

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