A Strongly Feasible Evolution Program for non-linear optimization of Network Flows

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

  author =       "Nesa Ilich",
  title =        "A Strongly Feasible Evolution Program for non-linear
                 optimization of Network Flows",
  school =       "Department of Civil and Geological Sciences,
                 University of Manitoba",
  year =         "2000",
  address =      "Winnipeg, Canada",
  month =        oct,
  email =        "NIlich@mail.com",
  keywords =     "genetic algorithms, genetic programming, Evolution
                 Programs, Network Flows, Non-Linear Constraints",
  URL =          "http://mspace.lib.umanitoba.ca/bitstream/1993/1759/1/NQ57510.pdf",
  size =         "163 pages",
  abstract =     "This thesis describes the main features of a Strongly
                 Feasible Evolution Program (SFEP) for solving network
                 flow programs that can be non-linear both in the
                 constraints and in the objective function. The approach
                 is a hybrid of a network flow algorithm and an
                 evolution program. Network flow theory is used to help
                 conduct the search exclusively within the feasible
                 region, while progress towards optimal points in the
                 search space is achieved using evolution programming
                 mechanisms such as recombination and mutation. The
                 solution procedure is based on a recombination operator
                 in which all parents in a small mating pool have equal
                 chance of contributing their genetic material to an
                 offspring. When an offspring is created with better
                 fitness value than that of the worst parent, the worst
                 parent is discarded from the mating pool while the
                 offspring is placed in it. The main contributions are
                 in the massive parallel initialization procedure which
                 creates only feasible solutions with simple heuristic
                 rules that increase chances of creating solutions with
                 good fitness values for the initial mating pool, and
                 the gene therapy procedure which fixes {"}defective
                 genes{"} ensuring that the offspring resulting from
                 recombination is always feasible. Both procedures use
                 the properties of network flows. Tests were conducted
                 on a number of previously published transportation
                 problems with 49 and 100 decision variables, and on two
                 problems involving water resources networks with
                 complex non-linear constraints with up to 1500
                 variables. Convergence to equal or better solutions was
                 achieved with often less than one tenth of the previous
                 computational efforts.",
  notes =        "Bighorn/Brazeau hydro power. Brantas river basin in
                 east Java.


Genetic Programming entries for Nesa Ilich