Dynamic Fitness Functions for Genetic Improvement in Compilers and Interpreters

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Oliver Krauss and Hanspeter Moessenboeck and 
                 Michael Affenzeller",
  title =        "Dynamic Fitness Functions for Genetic Improvement in
                 Compilers and Interpreters",
  booktitle =    "5th edition of GI @ GECCO 2018",
  year =         "2018",
  editor =       "Brad Alexander and Saemundur O. Haraldsson and 
                 Markus Wagner and John R. Woodward and Shin Yoo",
  pages =        "1590--1597",
  address =      "Kyoto, Japan",
  month =        "15-19 " # jul,
  organisation = "ACM SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, Fitness Functions, Test Driven
                 Verification, Test Complexity",
  URL =          "http://www.cs.stir.ac.uk/events/gecco-gi-2018/papers/dynamic_fitness_functions_for_genetic_improvement_in_compilers_and_interpreters.pdf",
  DOI =          "doi:10.1145/3205651.3208308",
  size =         "8 pages",
  abstract =     "When attempting to improve the non-functional
                 requirements of software, specifically run-time
                 performance of code, an important requirement is to
                 preserve the correctness of the optimized code.
                 Additionally when attempting to integrate Genetic
                 Improvement into a compiler or interpreter, the large
                 search spaces resulting from the amount of operators
                 and operands a language provides needs to be dealt
                 with. This publication explores dynamic fitness
                 functions as a foundation for a use in Genetic
                 Improvement to optimize programs. An approach of using
                 a test suite to verify code correctness in the Truffle
                 Framework and Graal Compiler is presented. Two types of
                 fitness functions are explored, which split the test
                 suite according to their complexity and attempt to
                 generate correct solutions with a growing set of
                 increasingly complex tests. One of them increases the
                 amount of tests sequentially over several iterations.
                 The parallel fitness function attempts to split a test
                 suite and to re-combine the results with increasingly
                 large suites. The results show that these functions
                 only marginally improve the fitness landscape on their
                 own, but show that more partially correct solutions can
                 be found with dynamic fitness functions. In the future,
                 our approach may be improved by implementing specific
                 crossover and mutator operations to accompany the
                 dynamic fitness functions.",
  notes =        "http://www.cs.stir.ac.uk/events/gecco-gi-2018/cfp.html",

Genetic Programming entries for Oliver Krauss Hanspeter Moessenboeck Michael Affenzeller