A Study of Genetic Programming and Grammatical Evolution for Automatic Object-Oriented Programming: A Focus on the List Data Structure

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

@InProceedings{Igwe:2015:NaBIC,
  title =        "A Study of Genetic Programming and Grammatical
                 Evolution for Automatic Object-Oriented Programming:
                 {A} Focus on the List Data Structure",
  author =       "Kevin Igwe and Nelishia Pillay",
  booktitle =    "Advances in Nature and Biologically Inspired
                 Computing: Proceedings of the 7th World Congress on
                 Nature and Biologically Inspired Computing
                 (NaBIC2015)",
  publisher =    "Springer",
  editor =       "Nelishia Pillay and Andries P. Engelbrecht and 
                 Ajith Abraham and Mathys C. du Plessis and Vaclav Snasel and 
                 Azah Kamilah Muda",
  year =         "2015",
  volume =       "419",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "151--163",
  address =      "Pietermaritzburg, South Africa",
  month =        dec # " 01-03",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution object-oriented programming, grammar, ADF,
                 OOGE, GOOGE, GE",
  isbn13 =       "978-3-319-27400-3",
  DOI =          "doi:10.1007/978-3-319-27400-3_14",
  abstract =     "Automatic programming is a concept which until today
                 has not been fully achieved using evolutionary
                 algorithms. Despite much research in this field, a lot
                 of the concepts remain unexplored. The current study is
                 part of ongoing research aimed at using evolutionary
                 algorithms for automatic programming. The performance
                 of two evolutionary algorithms, namely, genetic
                 programming and grammatical evolution are compared for
                 automatic object-oriented programming. Genetic
                 programming is an evolutionary algorithm which searches
                 a program space for a solution program. A program
                 generated by genetic programming is executed to yield a
                 solution to the problem at hand. Grammatical evolution
                 is a variation of genetic programming which adopts a
                 genotype-phenotype distinction and uses grammars to map
                 from a genotypic space to a phenotypic (program) space.
                 The study implements and tests the abilities of these
                 approaches as well as a further variation of genetic
                 programming, namely, object-oriented genetic
                 programming, for automatic object-oriented programming.
                 The application domain used to evaluate these
                 approaches is the generation of abstract data types,
                 specifically the class for the list data structure. The
                 study also compares the performance of the algorithms
                 when human programmer problem domain knowledge is
                 incorporated and when such knowledge is not
                 incorporated. The results show that grammatical
                 evolution performs better than genetic programming and
                 object-oriented genetic programming, with
                 object-oriented genetic programming outperforming
                 genetic programming. Future work will focus on
                 evolution of programs that use the evolved classes.",
}

Genetic Programming entries for Kevin C Igwe Nelishia Pillay

Citations