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Title: A study of the Lamarckian evolution of recurrent neural networks
Authors: Ku, KWC
Mak, MW 
Siu, WC 
Issue Date: Apr-2000
Source: IEEE transactions on evolutionary computation, Apr. 2000, v. 4, no. 1, p. 31-42
Abstract: Many frustrating experiences have been encountered when the training of neural networks by local search methods becomes stagnant at local optima. This calls for the development of more satisfactory search methods such as evolutionary search. However, training by evolutionary search can require a long computation time. In certain situations, using Lamarckian evolution, local search and evolutionary search can complement each other to yield a better training algorithm. This paper demonstrates the potential of this evolutionary-learning synergy by applying it to train recurrent neural networks in an attempt to resolve a long-term dependency problem and the inverted pendulum problem. This work also aims at investigating the interaction between local search and evolutionary search when they are combined. It is found that the combinations are particularly efficient when the local search is simple. In the case where no teacher signal is available for the local search to learn the desired task directly, the paper proposes introducing a related local task for the local search to learn, and finds that this approach is able to reduce the training time considerably.
Keywords: Evolutionary computation
Lamarckian evolution
Recurrent neural networks
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on evolutionary computation 
ISSN: 1089-778X (print)
1941-0026 (online)
DOI: 10.1109/4235.843493
Rights: © 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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