TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Abstract

Abstract This article proposes a genetic algorithm (GA)-enhanced weight optimization method for temporal convolutional network (TCN-GAWO). TCN-GAWO combines the evolutionary process of the genetic algorithm with the gradient-based training and can achieve higher predication/fitting accuracy than traditional temporal convolutional network (TCN). Performances of TCN-GAWO are also more stable. In TCN-GAWO, multiple TCNs are generated with random initial weights first, then these TCNs are trained individually for given epochs, next the selection-crossover-mutation procedure is applied among TCNs to get the evolved offspring. Gradient-based training and selection-crossover-mutation are taken in turns until convergence. The TCN with the optimal performance is then selected. Performances of TCN-GAWO are thoroughly evaluated using realistic engineering data, including C-MAPSS dataset provided by NASA and jet engine lubrication oil dataset provided by airlines. Experimental results show that TCN-GAWO outperforms existing methods for both datasets, demonstrating the effectiveness and the wide range applicability of the proposed method in solving time series problems.