On the use of multi-objective evolutionary algorithms for survival analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Christian Setzkorn and Azzam F. G. Taktak and 
                 Bertil E. Damato",
  title =        "On the use of multi-objective evolutionary algorithms
                 for survival analysis",
  journal =      "BioSystems",
  year =         "2007",
  volume =       "87",
  number =       "1",
  pages =        "31--48",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Survival
                 analysis, Evolutionary algorithms, Radial basis
                 function networks",
  URL =          "http://pcwww.liv.ac.uk/~afgt/SetzkornBiosys.pdf",
  DOI =          "doi:10.1016/j.biosystems.2006.03.002",
  abstract =     "This paper proposes and evaluates a multi-objective
                 evolutionary algorithm for survival analysis. One aim
                 of survival analysis is the extraction of models from
                 data that approximate lifetime/failure time
                 distributions. These models can be used to estimate the
                 time that an event takes to happen to an object. To use
                 of multi-objective evolutionary algorithms for survival
                 analysis has several advantages. They can cope with
                 feature interactions, noisy data, and are capable of
                 optimising several objectives. This is important, as
                 model extraction is a multi-objective problem. It has
                 at least two objectives, which are the extraction of
                 accurate and simple models. Accurate models are
                 required to achieve good predictions. Simple models are
                 important to prevent overfitting, improve the
                 transparency of the models, and to save computational
                 resources. Although there is a plethora of evolutionary
                 approaches to extract models for classification and
                 regression, the presented approach is one of the first
                 applied to survival analysis. The approach is evaluated
                 on several artificial datasets and one medical dataset.
                 It is shown that the approach is capable of producing
                 accurate models, even for problems that violate some of
                 the assumptions made by classical approaches.",

Genetic Programming entries for Christian Setzkorn Azzam F G Taktak Bertil E Damato