CTRNN Parameter Learning using Differential Evolution
De Falco Ivanoe, Antonio Della Cioppa, Francesco Donnarumma, Domenico Maisto, Roberto Prevete, Ernesto Tarantino · Frontiers in artificial intelligence and applications · 2008
Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems.