%0 Book Section %1 Schmidt:2006:GPTP %A Schmidt, Michael D. %A Lipson, Hod %B Genetic Programming Theory and Practice IV %C Ann Arbor %D 2006 %E Riolo, Rick L. %E Soule, Terence %E Worzel, Bill %I Springer %K algorithms, genetic programming %P - %T Co-evolving Fitness Predictors for Accelerating and Reducing Evaluations %V 5 %X We investigate co-evolutionary GP that that co-evolves fitness predictors in order to reduce the computational cost of evolution and/or reduced the number of evaluations required. Fitness predictors are light objects which, given an evolving individual, heuristically approximate its true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply coevolution of fitness predictors to symbolic regression and measure its impact. Our results show that a small computational investment in co-evolving fitness predictors greatly enhances both speed and convergence of individual solutions while reducing the computational effort overall. Finally we apply fitness prediction to interactive evolution of pen stroke drawings. These results show that fitness prediction is extremely effective at modelling user preference while minimising the sampling on the user to fewer than ten prompts. %& 17 %@ 0-387-33375-4

@incollection{Schmidt:2006:GPTP, abstract = {We investigate co-evolutionary GP that that co-evolves fitness predictors in order to reduce the computational cost of evolution and/or reduced the number of evaluations required. Fitness predictors are light objects which, given an evolving individual, heuristically approximate its true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply coevolution of fitness predictors to symbolic regression and measure its impact. Our results show that a small computational investment in co-evolving fitness predictors greatly enhances both speed and convergence of individual solutions while reducing the computational effort overall. Finally we apply fitness prediction to interactive evolution of pen stroke drawings. These results show that fitness prediction is extremely effective at modelling user preference while minimising the sampling on the user to fewer than ten prompts.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Ann Arbor}, author = {Schmidt, Michael D. and Lipson, Hod}, biburl = {https://www.bibsonomy.org/bibtex/221860b99f7d4c3a7962756b120cee10d/brazovayeye}, booktitle = {Genetic Programming Theory and Practice {IV}}, chapter = 17, editor = {Riolo, Rick L. and Soule, Terence and Worzel, Bill}, interhash = {50584e0a25c495fcc6e30cc7ae3066f9}, intrahash = {21860b99f7d4c3a7962756b120cee10d}, isbn = {0-387-33375-4}, keywords = {algorithms, genetic programming}, month = {11-13 May}, notes = {part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop}, pages = {-}, publisher = {Springer}, series = {Genetic and Evolutionary Computation}, timestamp = {2008-06-19T17:51:07.000+0200}, title = {Co-evolving Fitness Predictors for Accelerating and Reducing Evaluations}, volume = 5, year = 2006 }