### Abstract

We apply evolutionary computations to Hopfield's neural network model of associative memory. In the Hopfield model, almost infinite number of combinations of synaptic weights give a network a function of associative memory. Furthermore, there is a trade-off between the storage capacity and size of basin of attraction. Therefore, the model can be thought of as a test suite of multi-modal and/or multi-objective function optimization. As preliminary stages, we investigate the basic behaviors of associative memory under simple evolutionary processes. In this paper, we present some experiments using an evolution strategy.

Original language | English |
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Title of host publication | Proceedings of the IEEE Conference on Evolutionary Computation, ICEC |

Editors | Anon |

Publisher | IEEE |

Pages | 679-683 |

Number of pages | 5 |

Publication status | Published - 1997 |

Externally published | Yes |

Event | Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 - Indianapolis, IN, USA Duration: Apr 13 1997 → Apr 16 1997 |

### Other

Other | Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 |
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City | Indianapolis, IN, USA |

Period | 4/13/97 → 4/16/97 |

### All Science Journal Classification (ASJC) codes

- Computer Science(all)
- Engineering(all)

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## Cite this

Imada, A., & Araki, K. (1997). Application of an evolution strategy to the Hopfield model of associative memory. In Anon (Ed.),

*Proceedings of the IEEE Conference on Evolutionary Computation, ICEC*(pp. 679-683). IEEE.