Teams of Genetic Predictors for Inverse Problem Solving

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.