Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
Genetic Programming and Evolvable Machines
Non-stationarity and high-order scaling in TCP flow arrivals: a methodological analysis
ACM SIGCOMM Computer Communication Review
Solving differential equations with genetic programming
Genetic Programming and Evolvable Machines
Flexible neural trees ensemble for stock index modeling
Neurocomputing
Inference of differential equation models by genetic programming
Information Sciences: an International Journal
Information Sciences: an International Journal
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
Information Sciences: an International Journal
On the use of cross-validation for time series predictor evaluation
Information Sciences: an International Journal
Hi-index | 0.07 |
This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.