An autonomous GP-based system for regression and classification problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Mihai Oltean and Laura Diosan",
  title =        "An autonomous GP-based system for regression and
                 classification problems",
  journal =      "Applied Soft Computing",
  volume =       "9",
  number =       "1",
  pages =        "49--60",
  year =         "2009",
  ISSN =         "1568-4946",
  DOI =          "DOI:10.1016/j.asoc.2008.03.008",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Adaptive
                 strategies, Autonomous systems, Symbolic regression,
  abstract =     "The aim of this research is to develop an autonomous
                 system for solving data analysis problems. The system,
                 called Genetic Programming-Autonomous Solver (GP-AS)
                 contains most of the features required by an autonomous
                 software: it decides if it knows or not how to solve a
                 particular problem, it can construct solutions for new
                 problems, it can store the created solutions for later
                 use, it can improve the existing solutions in the
                 idle-time it can efficiently manage the computer
                 resources for fast running speed and it can detect and
                 handle failure cases. The generator of solutions for
                 new problems is based on an adaptive variant of Genetic
                 Programming. We have tested this part by solving some
                 well-known problems in the field of symbolic regression
                 and classification. Numerical experiments show that the
                 GP-AS system is able to perform very well on the
                 considered test problems being able to successfully
                 compete with standard GP having manually set

Genetic Programming entries for Mihai Oltean Laura Diosan