Multi-chromosomal CGP-evolved RNN for signal reconstruction - Khan, Nadia Masood; Khan, Gul Muhammad (Neural computing & applications, 2021)
A multi-chromosomal structure for neuro-evolution of recurrent neural networks (RNNs) with Cartesian genetic programming (CGP) architecture is presented to develop a signal reconstruction model. The group behaviour of multi-chromosomes evolved together is explored in terms of computational and structural complexity, and accurate reconstruction of the corrupted signal. Signal reconstruction modelling is done through prediction of missing samples, and a sliding window mechanism is utilized to recover long segments of missing samples in periodic and aperiodic corrupted signals. The proposed model is compared with the conventional single chromosome-based Cartesian Genetic Programming-evolved Recurrent Neural Network (CGPRNN) architecture as well as state-of-the-art machine learning algorithms, i.e. linear regression, random forest and long short-term memory deep learning neural network. Diverse network architectures of the proposed Multi-Chromosomal Cartesian Genetic Programming-evolved Recurrent Neural Network (MC-CGPRNN) are evolved to explore the robustness of algorithm in terms of optimization of hyper parameters, evolutionary speed and generalization ability. The proposed algorithm is trained and tested on speech signal with both periodic and aperiodic noise. The best trained network is also evaluated on standard music signals achieving remarkable results. Experimental results show that the proposed MC-CGPRNN algorithm achieved SNR of 25.23 dB for speech signal, 29.92 dB for guitar signal and 28.45 dB for flute signal in case of 50% missing samples showing best SNR improvement in comparison with its counterparts for time domain signal reconstruction.