Dynamic Fitness Functions for Genetic Improvement in Compilers and Interpreters
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Krauss:2018:GI5,
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author = "Oliver Krauss and Hanspeter Moessenboeck and
Michael Affenzeller",
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title = "Dynamic Fitness Functions for Genetic Improvement in
Compilers and Interpreters",
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booktitle = "5th edition of GI @ GECCO 2018",
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year = "2018",
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editor = "Brad Alexander and Saemundur O. Haraldsson and
Markus Wagner and John R. Woodward and Shin Yoo",
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pages = "1590--1597",
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address = "Kyoto, Japan",
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month = "15-19 " # jul,
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organisation = "ACM SIGEvo",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, genetic
improvement, Fitness Functions, Test Driven
Verification, Test Complexity",
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URL = "http://www.cs.stir.ac.uk/events/gecco-gi-2018/papers/dynamic_fitness_functions_for_genetic_improvement_in_compilers_and_interpreters.pdf",
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DOI = "doi:10.1145/3205651.3208308",
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size = "8 pages",
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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.",
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notes = "http://www.cs.stir.ac.uk/events/gecco-gi-2018/cfp.html",
- }
Genetic Programming entries for
Oliver Krauss
Hanspeter Moessenboeck
Michael Affenzeller
Citations