Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
High-performance, parallel, stack-based genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Environmental robustness in multi-agent teams
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolution of team composition in multi-agent systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Density estimation with genetic programming for inverse problem solving
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Training time and team composition robustness in evolved multi-agent systems
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
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Genetic Programming (GP) has been shown to be a good method of predicting functions that solve inverse problems. In this context, a solution given by GP generally consists of a sole predictor. In contrast, Stack-based GP systems manipulate structures containing several predictors, which can be considered as teams of predictors. Work in Machine Learning reports that combining predictors gives good results in terms of both quality and robustness. In this paper, we use Stack-based GP to study different cooperations between predictors. First, preliminary tests and parameter tuning are performed on two GP benchmarks. Then, the system is applied to a real-world inverse problem. A comparative study with standard methods has shown limits and advantages of teams prediction, leading to encourage the use of combinations taking into account the response quality of each team member.