Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Parallel genetic programming and its application to trading model induction
Parallel Computing
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
Genetic Programming and Evolvable Machines
An Empirical Study of Multipopulation Genetic Programming
Genetic Programming and Evolvable Machines
International Journal of Systems Science
GP-Sumo: Using genetic programming to evolve sumobots
Genetic Programming and Evolvable Machines
Use of genetic programming to diagnose venous thromboembolism in the emergency department
Genetic Programming and Evolvable Machines
System identification using hierarchical fuzzy neural networks with stable learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Lateral jet interaction model identification based on genetic programming
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Hi-index | 0.00 |
To realize simultaneous identification of both structures and parameters of stochastic nonlinear systems, multi-population parallel genetic programming (GP) was employed. Object systems were represented by nonlinear autoregressive with exogenous inputs (NARX) and nonlinear autoregressive moving average with exogenous inputs (NARMAX) polynomial models, multi-objective fitness definition was used to restrict sizes of individuals during the evolution. For all examples, multi-population parallel GP found accurate models for object systems, simultaneously identified structures and parameters. In comparison with traditional single-population GP, multi-population GP showed a more competitive performance in avoiding premature convergence, and was much more efficient in searching for good models for object systems. From identification results, it can be concluded that multi-population parallel GP is good at handling complex stochastic nonlinear system identification problems and is superior to other existing identification methods.