Optimization of neural networks through grammatical evolution and a genetic algorithm
作者：
Highlights：
• Neural codification confers scalability and search space reduction.
• The parallel genome scan engine increases the implicit parallelism of the GA.
• Our approach rewards economical ANNs which have better generalization capacity.
• Reduction in chromosome length from 512 to 180 bits.
• Our NEA outperforms other methods, providing the lowest computational effort.
摘要
•Neural codification confers scalability and search space reduction.•The parallel genome scan engine increases the implicit parallelism of the GA.•Our approach rewards economical ANNs which have better generalization capacity.•Reduction in chromosome length from 512 to 180 bits.•Our NEA outperforms other methods, providing the lowest computational effort.
论文关键词：Evolutionary computation,Neural networks,Grammatical evolution
论文评审过程：Received 17 August 2015, Revised 18 January 2016, Accepted 8 March 2016, Available online 16 March 2016, Version of Record 6 April 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.012
