Genetic Programming Bias with Software Performance Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{BCK-thesis,
  author =       "Brendan Cody-Kenny",
  title =        "Genetic Programming Bias with Software Performance
                 Analysis",
  school =       "Trinity College Dublin",
  year =         "2015",
  address =      "Ireland",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, SBSE, locoGP, Java, AST",
  URL =          "http://www.tara.tcd.ie/bitstream/handle/2262/76251/BCK.thesis.april.2016%5b1%5d.pdf",
  URL =          "http://www.tara.tcd.ie/handle/2262/76251",
  size =         "190 pages",
  abstract =     "The complexities of modern software systems make their
                 engineering costly and time consuming. This thesis
                 explores and develops techniques to improve software by
                 automating re-design. Source code can be randomly
                 modified and subsequently tested for correctness to
                 search for improvements in existing software. By
                 iteratively selecting useful programs for modification
                 a randomised search of program variants can be guided
                 toward improved programs. Genetic Programming (GP) is a
                 search algorithm which crucially relies on selection to
                 guide the evolution of programs. Applying GP to
                 software improvement represents a scalability challenge
                 given the number of possible modification locations in
                 even the smallest of programs.

                 The problem addressed in this thesis is locating
                 performance improvements within programs. By randomly
                 modifying a location within a program and measuring the
                 change in performance and functionality we determine
                 the probability of finding a performance improvement at
                 that location under further modification. Locating
                 performance improvements can be performed during GP as
                 GP relies on mutation.

                 A probabilistic overlay of bias values for modification
                 emerges as GP progresses and the software evolves.
                 Measuring different aspects of program change can
                 fine-tune the GP algorithm To focus on code which is
                 particularly relevant to the measured aspect. Measuring
                 execution cost reduction can indicate where an
                 improvement is likely to exist and increase the chances
                 of finding an improvement during GP.",
  notes =        "Appendix B Code Listings http://rosettacode.org Bubble
                 Sort, Shell Sort, Selection Sort, Selection Sort 2,
                 Radix Sort, Quick Sort, Merge Sort, Deceptive Bubble
                 Sort, Insertion Sort, Heap Sort, Cocktail Sort, Huffman
                 Codebook Generator.

                 Supervisor Stephen Barrett",
}

Genetic Programming entries for Brendan Cody-Kenny

Citations