Created by W.Langdon from gp-bibliography.bib Revision:1.7975
Results: In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyse whole-genome data.",
Preliminary Version: Technical Report 24/2007, SFB 475, Universitat Dortmund, Germany. http://hdl.handle.net/2003/25875 http://dx.doi.org/10.17877/DE290R-14226
Disjunctive Normal Form (bit set fast implementation). Variable population size (those which are not dominated) Pareto (3 objectives: size TP, TN). No fitness sharing? Non-standard selection. crossover. 5 DNF specific mutations.
GPAS better than Logic regression, CART, Bagging, Random Forests on GENICA HapMap and (Random) Simulated data.",
Genetic Programming entries for Robin Nunkesser Thorsten Bernholt Holger Schwender Katja Ickstadt Ingo Wegener