Symbiotic Coevolution of Artificial Neural Networks and Training Data Sets
暂无分享,去创建一个
[1] W. Daniel Hillis,et al. Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .
[2] J. Lovelock. The Ages of Gaia: A Biography of Our Living Earth , 1988 .
[3] Felicity A. W. George,et al. A Study in Set Recombination , 1993, ICGA.
[4] Lorien Y. Pratt,et al. Comparing Biases for Minimal Network Construction with Back-Propagation , 1988, NIPS.
[5] Helio J. C. Barbosa. A Coevolutionary Genetic Algorithm for a Game Approach to Structural Optimization , 1997, ICGA.
[6] Peter M. Todd,et al. Designing Neural Networks using Genetic Algorithms , 1989, ICGA.
[7] Helmut A. Mayer. ptGAs - Genetic Algorithms Using Promoter/Terminator Sequences - Evolution of Number, Size, and Location of Parameters and Parts of the Representation , 1997 .
[8] A. Wu. Non-coding DNA and floating building blocks for the genetic algorithm , 1996 .
[9] Reinhold Huber,et al. On the Role of Regularization Parameters in Fitness Functions for Evolutionary Designed Artificial N , 1996 .
[10] Roland Schwaiger,et al. Towards the evolution of training data sets for artificial neural networks , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).
[11] James R. Levenick. Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.
[12] Axel Roebel. The Dynamic Pattern Selection Algorithm: Effective Training and Controlled Generalization of Backpropagation Neural Networks , 1994 .
[13] Reinhold Huber,et al. netGEN - A Parallel System Generating Problem-Adapted Topologies of Artificial Neural Networks by Means of Genetic Algorithms , 1995 .
[14] Byoung-Tak Zhang,et al. Accelerated Learning by Active Example Selection , 1994, Int. J. Neural Syst..