Dynamic Programming Inspired Genetic Programming to Solve Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

@Article{oai:thesai.org:10.14569/IJACSA.2017.080463,
  author =       "Asim Darwaish and Hammad Majeed and M. Quamber Ali and 
                 Abdul Rafay",
  title =        "Dynamic Programming Inspired Genetic Programming to
                 Solve Regression Problems",
  journal =      "International Journal of Advanced Computer Science and
                 Applications (IJACSA)",
  publisher =    "The Science and Information (SAI) Organization",
  year =         "2017",
  volume =       "8",
  number =       "4",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computing, machine learning, fitness landscape,
                 semantic gp, symbolic regression and dynamic
                 decomposition of gp",
  bibsource =    "OAI-PMH server at thesai.org",
  description =  "International Journal of Advanced Computer Science and
                 Applications(IJACSA), 8(4), 2017",
  language =     "eng",
  oai =          "oai:thesai.org:10.14569/IJACSA.2017.080463",
  URL =          "http://thesai.org/Downloads/Volume8No4/Paper_63-Dynamic_Programming_Inspired_Genetic.pdf",
  DOI =          "doi:10.14569/IJACSA.2017.080463",
  abstract =     "The candidate solution in traditional Genetic
                 Programing is evolved through prescribed number of
                 generations using fitness measure. It has been observed
                 that, improvement of GP on different problems is
                 insignificant at later generations. Furthermore, GP
                 struggles to evolve on some symbolic regression
                 problems due to high selective pressure, where input
                 range is very small, and few generations are allowed.
                 In such scenarios stagnation of GP occurs and GP cannot
                 evolve a desired solution. Recent works address these
                 issues by using single run to reduce residual error
                 which is based on semantic concept. A new approach is
                 proposed called Dynamic Decomposition of Genetic
                 Programming (DDGP) inspired by dynamic programing. DDGP
                 decomposes a problem into sub problems and initiates
                 sub runs in order to find sub solutions. The algebraic
                 sum of all the sub solutions merge into an overall
                 solution, which provides the desired solution.
                 Experiments conducted on well known benchmarks with
                 varying complexities, validates the proposed approach,
                 as the empirical results of DDGP are far superior to
                 the standard GP. Moreover, statistical analysis has
                 been conducted using T test, which depicted significant
                 difference on eight datasets. Symbolic regression
                 problems where other variants of GP stagnates and
                 cannot evolve the required solution, DDGP is highly
                 recommended for such symbolic regression problems.",
  notes =        "National University of Computer and Emerging Sciences
                 FAST Islamabad, Pakistan",
}

Genetic Programming entries for Asim Darwaish Hammad Majeed M Quamber Ali Abdul Rafay

Citations