Title :
Lamarckian evolution of associative memory
Author :
Imada, Akira ; Araki, Keijiro
Author_Institution :
Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Ikoma, Japan
Abstract :
There has been a lot of research which applies evolutionary techniques to layered neural networks. However, their application to Hopfield neural networks remain few so far. We apply genetic algorithms to a fully connected Hopfield associative memory model. In an earlier paper, we reported that random weight matrices were evolved to store a number of patterns only by means of a simple genetic algorithm (A. Imada and K. Araki, 1995). We propose that the storage capacity can be enlarged by incorporating Lamarckian inheritance to the genetic algorithm
Keywords :
Hopfield neural nets; content-addressable storage; genetic algorithms; inheritance; Hopfield neural networks; Lamarckian evolution; Lamarckian inheritance; associative memory; evolutionary techniques; fully connected Hopfield associative memory model; genetic algorithms; layered neural networks; random weight matrices; simple genetic algorithm; storage capacity; Artificial neural networks; Associative memory; Computer simulation; Genetic algorithms; Hopfield neural networks; Information science; Neural networks; Neurons; Organisms; Transfer functions;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542682
