Behavioral Program Synthesis: Insights and Prospects

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Krzysztof Krawiec and Jerry Swan and 
                 Una-May O'Reilly",
  title =        "Behavioral Program Synthesis: Insights and Prospects",
  booktitle =    "Genetic Programming Theory and Practice XIII",
  year =         "2015",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and William P. Worzel and M. Kotanchek and 
                 A. Kordon",
  pages =        "169--183",
  address =      "Ann Arbor, USA",
  month =        "14-16 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming programming,
                 program behaviour, program semantics, multiobjective
                 evaluation, search driver, evaluation bottleneck",
  isbn13 =       "978-3-319-34223-8",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-34223-8_10",
  size =         "17 pages",
  abstract =     "Genetic programming (GP) is a stochastic, iterative
                 generate-and-test approach to synthesizing programs
                 from tests, i.e. examples of the desired input-output
                 mapping. The number of passed tests, or the total error
                 in continuous domains, is a natural objective measure
                 of a program's performance and a common yardstick when
                 experimentally comparing algorithms. In GP, it is also
                 by default used to guide the evolutionary search
                 process. An assumption that an objective function
                 should also be an efficient search driver is common for
                 all metaheuristics, such as the evolutionary algorithms
                 which GP is a member of. Programs are complex
                 combinatorial structures that exhibit even more complex
                 input-output behaviour, and in this chapter we discuss
                 why this complexity cannot be effectively reflected by
                 a single scalar objective. In consequence, GP
                 algorithms are systemically under informed about the
                 characteristics of programs they operate on, and pay
                 for this with unsatisfactory performance and limited
                 scalability. This chapter advocates behavioural program
                 synthesis, where programs are characterised by
                 informative execution traces that enable multifaceted
                 evaluation and substantially change the roles of
                 components in an evolutionary infrastructure. We
                 provide a unified perspective on past work in this
                 area, discuss the consequences of the behavioral
                 viewpoint, outlining the future avenues for program
                 synthesis and the wider application areas that lie
  notes =        "PANGEA


                 Part of \cite{Riolo:2015:GPTP} Published after the
                 workshop in 2016",

Genetic Programming entries for Krzysztof Krawiec Jerry Swan Una-May O'Reilly