Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
Fault diagnosis in power plant using neural networks
Information Sciences: an International Journal - Intelligent manufacturing and fault diagnosis (II). Soft computing approaches to fault diagnosis
An efficient simulation scheme for testing materials in a nondestructive manner
Information Sciences: an International Journal
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
Genetic programming in classifying large-scale data: an ensemble method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Thalassaemia classification by neural networks and genetic programming
Information Sciences: an International Journal
Information Sciences: an International Journal
A genetic programming framework for content-based image retrieval
Pattern Recognition
Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming
Expert Systems with Applications: An International Journal
Generating prediction rules for liquefaction through data mining
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Optimal depth estimation by combining focus measures using genetic programming
Information Sciences: an International Journal
Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming
Expert Systems with Applications: An International Journal
A hybrid computational approach to derive new ground-motion prediction equations
Engineering Applications of Artificial Intelligence
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Advances in Artificial Neural Systems
A new predictive model for compressive strength of HPC using gene expression programming
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Neurocomputing
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This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.