Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
No free lunch, program induction and combinatorial problems
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Grammar based crossover operator in genetic programming
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
This article proposes a context-free grammar to be used in grammar-guided genetic programming systems to automatically design feed-forward neural architectures. This grammar has three important features. The sentences that belong to the grammar are binary strings that directly encode all the valid neural architectures only. This rules out the appearance of illegal points in the search space. Second, the grammar has the property of being ambiguous and semantically redundant. Therefore, there are alternative ways of reaching the optimum. Third, the grammar starts by generating small networks. This way it can efficiently adapt to the complexity of the problem to be solved. From the results, it is clear that these three properties are beneficial to the convergence process of the grammar-guided genetic programming system.