On Linear Genetic Programming

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

@PhdThesis{B2005OLGP,
  title =        "On Linear Genetic Programming",
  author =       "Markus Brameier",
  month =        feb,
  year =         "2004",
  school =       "Fachbereich Informatik, Universit{\"a}t Dortmund",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 algorithms, Machine learning",
  URL =          "https://eldorado.uni-dortmund.de/bitstream/2003/20098/2/Brameierunt.pdf",
  URL =          "https://eldorado.uni-dortmund.de/bitstream/2003/20098/1/Brameier.ps",
  size =         "272 pages",
  abstract =     "The thesis is about linear genetic programming (LGP),
                 a machine learning approach that evolves computer
                 programs as sequences of imperative instructions. Two
                 fundamental differences to the more common tree-based
                 variant (TGP) may be identified. These are the
                 graph-based functional structure of linear genetic
                 programs, on the one hand, and the existence of
                 structurally noneffective code, on the other hand.The
                 two major objectives of this work comprise(1) the
                 development of more advanced methods and variation
                 operators to produce better and more compact program
                 solutions and (2) the analysis of general EA/GP
                 phenomena in linear GP, including intron code, neutral
                 variations, and code growth, among others.First, we
                 introduce efficient algorithms for extracting features
                 of the imperative and functional structure of linear
                 genetic programs.In doing so, especially the detection
                 and elimination of noneffective code during runtime
                 will turn out as a powerful tool to accelerate the
                 time-consuming step of fitness evaluation in
                 GP.Variation operators are discussed systematically for
                 the linear program representation. We will demonstrate
                 that so called effective instruction mutations achieve
                 the best performance in terms of solution quality.These
                 mutations operate only on the (structurally) effective
                 code and restrict the mutation step size to one
                 instruction.One possibility to further improve their
                 performance is to explicitly increase the probability
                 of neutral variations. As a second, more time-efficient
                 alternative we explicitly control the mutation step
                 size on the effective code (effective step
                 size).Minimum steps do not allow more than one
                 effective instruction to change its effectiveness
                 status. That is, only a single node may be connected to
                 or disconnected from the effective graph component. It
                 is an interesting phenomenon that, to some extent, the
                 effective code becomes more robust against destructions
                 over the generations already implicitly. A special
                 concern of this thesis is to convince the reader that
                 there are some serious arguments for using a linear
                 representation.In a crossover-based comparison LGP has
                 been found superior to TGP over a set of benchmark
                 problems. Furthermore, linear solutions turned out to
                 be more compact than tree solutions due to (1) multiple
                 usage of subgraph results and (2) implicit parsimony
                 pressure by structurally noneffective code.The
                 phenomenon of code growth is analysed for different
                 linear genetic operators. When applying instruction
                 mutations exclusively almost only neutral variations
                 may be held responsible for the emergence and
                 propagation of intron code. It is noteworthy that
                 linear genetic programs may not grow if all neutral
                 variation effects are rejected and if the variation
                 step size is minimum.For the same reasons effective
                 instruction mutations realize an implicit complexity
                 control in linear GP which reduces a possible negative
                 effect of code growth to a minimum.Another noteworthy
                 result in this context is that program size is strongly
                 increased by crossover while it is hardly influenced by
                 mutation even if step sizes are not explicitly
                 restricted. Finally, we investigate program teams as
                 one possibility to increase the dimension of genetic
                 programs. It will be demonstrated that much more
                 powerful solutions may be found by teams than by
                 individuals. Moreover, the complexity of team solutions
                 remains surprisingly small compared to individual
                 programs. Both is the result of specialisation and
                 cooperation of team members.",
  notes =        "Day of Submission: 2003-05-28, Committee: Wolfgang
                 Banzhaf and Martin Riedmiller and Peter Nordin.
                 \onlineAvailableT{https://eldorado.uni-dortmund.de/handle/2003/20098}{http://hdl.handle.net/2003/20098}{2007-08-17}",
}

Genetic Programming entries for Markus Brameier

Citations