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Abstract: This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GAs) for training recurrent neural networks (RNNs). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GAs... (Update)
Context of citations to this paper: More
.... In this paper, we focus on the former approach, since we found that it outperforms the latter approach in our previous studies [16] [18]. 2 We conjecture that the inefficiency of the latter approach is due to the fact that too many weights in the networks can be changed by...
.... 841, 849, 865, 898, 909, 910, 915, 916, 953] IEEE Transactions on Networking, 1071] IEEE Transactions on Neural Networks, [990, 766, 338, 1201, 108] IEEE Transactions on Pattern Analysis and Machine Intelligence, 812, 109] IEEE Transactions on Power Delivery, 1148] IEEE...
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BibTeX entry: (Update)
K. W. C. Ku, M. W. Mak, and W. C. Siu. Adding learning to cellular genetic algorithms for training recurrent neural networks. IEEE Transactions on Neural Networks, 10(2):239--252, 1999. http://citeseer.ist.psu.edu/ku98adding.html More
@article{ ku99adding,
author = "K. W. C. Ku and M. W. Mak and W. C. Siu",
title = "Adding Learning to Cellular Genetic Algorithms for Training Recurrent Neural Networks",
journal = "IEEE-NN",
volume = "10",
number = "2",
month = "March",
pages = "239",
year = "1999",
url = "citeseer.ist.psu.edu/ku98adding.html" }
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