A constraint-guided method with evolutionary algorithms for economic problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Jin2009924,
  author =       "Nanlin Jin and Edward Tsang and Jin Li",
  title =        "A constraint-guided method with evolutionary
                 algorithms for economic problems",
  journal =      "Applied Soft Computing",
  volume =       "9",
  number =       "3",
  pages =        "924--935",
  year =         "2009",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2008.11.006",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-4V0TCY0-6/2/6b82133b94fa2c3580d4e43064120400",
  keywords =     "genetic algorithms, genetic programming, Constraint
                 satisfaction, Economic problems",
  abstract =     "This paper presents an evolutionary algorithms based
                 constrain-guided method (CGM) that is capable of
                 handling both hard and soft constraints in optimization
                 problems. While searching for constraint-satisfied
                 solutions, the method differentiates candidate
                 solutions by assigning them with different fitness
                 values, enabling favorite solutions to be distinguished
                 more likely and more effectively from unfavoured
                 ones.

                 We illustrate the use of CGM in solving two economic
                 problems with optimization involved: (1) searching
                 equilibriums for bargaining problems; (2) reducing the
                 rate of failure in financial prediction problems. The
                 efficacy of the proposed CGM is analysed and compared
                 with some other computational techniques, including a
                 repair method and a penalty method for the problem (1),
                 a linear classifier and three neural networks for the
                 problem (2), respectively. Our studies here suggest
                 that the evolutionary algorithms based CGM compares
                 favorably against those computational approaches.",
}

Genetic Programming entries for Nanlin Jin Edward P K Tsang Jin Li

Citations