Designing neural networks by a combination of structural learning and genetic algorithms
説明
Kitano proposed to use GA with graph encoding method to have good scalability for hierarchical networks. However, this is not applicable to recurrent networks. The authors propose to use a combination of a structural learning with forgetting(SLF) and GA for designing recurrent neural networks; the former generates quasi-optimal recurrent network structure and the latter prevents local minima by global search. Its applications to two kinds of time series data well demonstrate the superiority to SLF and to a combination of GA and BPTT.