A Lamarckian Approach for Neural Network Training
Neural Processing Letters
Advances in evolutionary computing
Adaptation for parallel memetic algorithm based on population entropy
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The influence of learning on evolution: A mathematical framework
Artificial Life
Acquiring visibly intelligent behavior with example-guided neuroevolution
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Lamarckian neuroevolution for visual control in the quake II environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Backpropagation without human supervision for visual control in quake II
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Memetic algorithm with extended neighborhood search for capacitated arc routing problems
IEEE Transactions on Evolutionary Computation
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
Imitation tendencies of local search schemes in baldwinian evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Accelerating evolution via egalitarian social learning
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Memetic algorithms for de novo motif-finding in biomedical sequences
Artificial Intelligence in Medicine
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Training neural networks 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 a related local task for the local search to learn, and finds that this approach is able to reduce the training time considerably