@misc{Wilbik_Anna_Genetic_2004,
 author={Wilbik, Anna},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2004},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={In this paper the Hopfield neural network model of associative memory that uses evolutionary approach as a learning method is analyzed. Several types of genetic algorithms will be investigated. In resulting networks quality measures (such as storage capacity, error correcting capabilities and the usage of additional knowledge) will be carefully examined. The basic criterion for algorithm’s evaluation is the storage capacity. The well-known Hebbian rule provides the capacity to be 15% of the number of the neurons. It has been shown that genetic algorithms allow us to improve this result.},
 type={Text},
 title={Genetic algorithms in learning methods of associative memory},
 URL={http://www.rcin.org.pl/Content/139584/PDF/RB-2004-29.pdf},
 keywords={Genetic algorithm, Hopfields model, Storage capacity, Basin of attraction},
}