The influence of mutation on population dynamics in multiobjective genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Khaled Badran and Peter I. Rockett",
  title =        "The influence of mutation on population dynamics in
                 multiobjective genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2010",
  volume =       "11",
  number =       "1",
  pages =        "5--33",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming,
                 Multiobjective genetic programming, Population
                 collapse, Mutation, Population dynamics, MOGP, bloat",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-009-9084-3",
  abstract =     "Using multiobjective genetic programming with a
                 complexity objective to overcome tree bloat is usually
                 very successful but can sometimes lead to undesirable
                 collapse of the population to all single-node trees. In
                 this paper we report a detailed examination of why and
                 when collapse occurs. We have used different types of
                 crossover and mutation operators (depth-fair and
                 sub-tree), different evolutionary approaches
                 (generational and steady-state), and different datasets
                 (6-parity Boolean and a range of benchmark machine
                 learning problems) to strengthen our conclusion. We
                 conclude that mutation has a vital role in preventing
                 population collapse by counterbalancing parsimony
                 pressure and preserving population diversity. Also,
                 mutation controls the size of the generated individuals
                 which tends to dominate the time needed for fitness
                 evaluation and therefore the whole evolutionary
                 process. Further, the average size of the individuals
                 in a GP population depends on the evolutionary approach
                 employed. We also demonstrate that mutation has a wider
                 role than merely culling single-node individuals from
                 the population; even within a diversity-preserving
                 algorithm such as SPEA2 mutation has a role in
                 preserving diversity.",
  notes =        "Steady-state algorithm depth-fair crossover/depth-fair

Genetic Programming entries for Khaled M S Badran Peter I Rockett