Evolving Teams of Predictors with Linear Genetic Programming

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Evolving Teams of Predictors with Linear Genetic Programming (2001)

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by Markus Brameier , Wolfgang Banzhaf

Venue:	Genetic Programming and Evolvable Machines
Citations:	37 - 3 self

BibTeX

@INPROCEEDINGS{Brameier01evolvingteams,
    author = {Markus Brameier and Wolfgang Banzhaf},
    title = {Evolving Teams of Predictors with Linear Genetic Programming},
    booktitle = {Genetic Programming and Evolvable Machines},
    year = {2001},
    pages = {381--407},
    publisher = {Kluwer}
}

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Abstract

This paper applies the evolution of GP teams to di#erent classification and regression problems and compares di#erent 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 team 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.

Keyphrases

linear genetic programming erent classification hybrid approach di erent method fast variant standard gp method generalization performance gp team linear gp regression problem cooperative team approach result team program evolution strategy team evolution common decision computational overhead realnumbered vector neural network improved training

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