Multi-Chromosomal Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{cavill_mcgp,
  author =       "Rachel Cavill",
  title =        "Multi-Chromosomal Genetic Programming",
  school =       "Department of Electronics, University of York",
  type =         "{PhD} Dissertation",
  year =         "2006",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=7&uin=uk.bl.ethos.437617",
  URL =          "http://phdtree.org/pdf/25505598-multi-chromosomal-genetic-programming/",
  abstract =     "Typically, computational models inspired by evolution
                 have comprised a single large structure, such as a
                 tree, string or graph, representing a single
                 chromosome. Through the use of evolutionary operators,
                 as mutation and recombination, over a number of
                 generations a satisfactory solution to the problem may
                 be found and the evolution halted. In natural,
                 biological systems, it is not so common to find
                 organisms which have only a single chromosome. Indeed,
                 it is only bacteria and other relatively simple
                 life-forms which can survive with only a single
                 chromosome structure. All animals and plants have a
                 much richer and more complex chromosome space, with not
                 only multiple chromosomes, but multiple copies of each
                 chromosome. Within artificial systems, sometimes, often
                 for very problem-specific reasons, multiple structures
                 are used which each make up part of the final solution.
                 More recently, with the area of co-evolution
                 successfully exploring the evolution of teams, further
                 steps along this path towards a richer representation
                 space have been investigated. This thesis investigates
                 the exploitation of evolution with multiple chromosomes
                 within computational models. By studying the biological
                 model presented to us in nature, and attempting to
                 extract the key mechanisms of multi chromosomal
                 evolution an artificial system which imitates these
                 mechanisms is developed. The system is designed to
                 allow evolution with any number of chromosomes, so that
                 experiments comparing evolution with a single
                 chromosome to that with many may be performed. This
                 work is not attempting to model biological evolution
                 but is inspired by it. As well as presenting a richer
                 representation space, the presence of multiple
                 chromosomes also permits more complex evolutionary
                 operators. For instance crossover may work between any
                 pair of chromosomes, or may be restricted to be allowed
                 only to occur between particular pairs of chromosomes.
                 Natural systems too, display a range of crossover
                 operators acting in different ways; therefore it is
                 important to study the implications of using different
                 crossover operators and to assess their relative
                 characteristics and advantages. To this end, this
                 thesis presents a system which allows evolution to
                 occur with a specified number of chromosomes,
                 conforming to k sets of n chromosomes. Using this
                 system, experiments are done over a range of standard
                 genetic programming benchmark problems to ascertain the
                 affects of increasing the number of chromosomes along
                 each of these two axis of variation. Further
                 experiments are conducted into the behaviour of the
                 crossover operator with this more complex
                 representation and various crossover operators are
                 evaluated within the system. Overall, it was found that
                 multiple chromosomes increase the performance of the
                 evolutionary system, insofar as better solutions were
                 obtained more quickly in the simulations. However, in
                 order to attain optimal increases both the number of
                 chromosomes and the number of copies of copies of each
                 in the system, need to be considered. The optimal
                 number of chromosomes is shown to be problem dependent,
                 but initial conclusions about how many chromosomes
                 different types of problems are likely to use are also
                 presented. Additionally, the crossover operator is
                 shown to work best when it is restricted only to work
                 with the exact same chromosome from the other parent.",
  notes =        "uk.bl.ethos.437617",
}

Genetic Programming entries for Rachel Cavill

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