Abstract
This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.
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Brameier, M., Banzhaf, W. Evolving Teams of Predictors with Linear Genetic Programming. Genetic Programming and Evolvable Machines 2, 381–407 (2001). https://doi.org/10.1023/A:1012978805372
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DOI: https://doi.org/10.1023/A:1012978805372