%0 Journal Article %1 brameier:2001:GPEM %A Brameier, Markus %A Banzhaf, Wolfgang %D 2001 %J Genetic Programming and Evolvable Machines %K algorithms, combination evolution genetic linear multiple of predictors, programming programming, teams, %N 4 %P 381--407 %R doi:10.1023/A:1012978805372 %T Evolving Teams of Predictors with Linear Genetic Programming %U http://citeseer.ist.psu.edu/411995.html %V 2 %X 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 real numbered 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. In particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution.

@article{brameier:2001:GPEM, 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 real numbered 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. In particular, the processing time of linear genetic programs is reduced significantly by removing intron code before program execution.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Brameier, Markus and Banzhaf, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2aa60510b9c8db7f49efd43f45e711655/brazovayeye}, doi = {doi:10.1023/A:1012978805372}, interhash = {620b842f2a69f9e7f6fe3be84d938b40}, intrahash = {aa60510b9c8db7f49efd43f45e711655}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {algorithms, combination evolution genetic linear multiple of predictors, programming programming, teams,}, month = {December}, notes = {Article ID: 386363}, number = 4, pages = {381--407}, size = {26 pages}, timestamp = {2008-06-19T17:36:54.000+0200}, title = {Evolving Teams of Predictors with Linear Genetic Programming}, url = {http://citeseer.ist.psu.edu/411995.html}, volume = 2, year = 2001 }