Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolutionary Computation
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dynamics of genetic programming and chaotic time series prediction
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Linear Genetic Programming
Long memory time series forecasting by using genetic programming
Genetic Programming and Evolvable Machines
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
Genetic programming has been successfully used for symbolic regression of time series data in a wide variety of applications. However, previous approaches have not taken into account the presence of multiple-time-scale dynamics despite their prevalence in both natural and artificial dynamical systems. Here, we propose an algorithm that first decomposes data from such systems into components with dynamics at different time scales and then performs symbolic regression separately for each scale. Results show that this divide-and-conquer approach improves the accuracy and efficiency with which genetic programming can be used to reverse-engineer multiple-time-scale dynamical systems.