A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Declarative and Preferential Bias in GP-based Scientific Discovery
Genetic Programming and Evolvable Machines
Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Proceedings of the 6th International Conference on Genetic Algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
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Correct estimation of suspended sediment concentration carried by a river is very important for many water resources projects. The application of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, for suspended sediment concentration estimation is proposed in this paper. The LGP is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, operated by the US Geological Survey (USGS) are used as case studies. The root mean square errors (RMSE) and determination coefficient (R^2) statistics are used for evaluating the accuracy of the models. Comparison of the results indicated that the LGP performs better than the neuro-fuzzy, neural networks and rating curve models. For the Rio Valenciano and Quebrada Blanca Stations, it is found that the LGP models with RMSE=44.4mg/l, R^2=0.910 and RMSE=13.9mg/l, R^2=0.952 in test period is superior in estimating daily suspended sediment concentrations than the best accurate neuro-fuzzy model with RMSE=52.0mg/l, R^2=0.876 and RMSE=17.9mg/l, R^2=0.929, respectively.