Deep Genetic Programming Trees are Robust
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{langdon:2022:TELO,
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author = "William B. Langdon",
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title = "Deep Genetic Programming Trees are Robust",
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journal = "ACM Transactions on Evolutionary Learning and
Optimization",
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year = "2022",
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volume = "2",
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number = "2",
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articleno = "6",
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month = jun,
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keywords = "genetic algorithms, genetic programming, heritability,
information theory, information funnels, sandpile 1/f
powerlaw, self-organised criticality, SOC, self-similar
fractal, GP fitness landscape, evolvability, mutational
robustness, neutral networks, SBSE, software
robustness, correctness attraction, diversity, software
testing, theory of bloat, introns, error hiding,
invisible faults, disruption propagation failure, DPF,
FEP",
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ISSN = "2688-299X",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2022_TELO.pdf",
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DOI = "doi:10.1145/3539738",
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size = "34 pages",
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abstract = "We sample the genetic programming tree search space
and show it is smooth since many mutations on many test
cases have little or no fitness impact. We generate
uniformly at random high order polynomials composed of
12500 and 750000 additions and multiplications and
follow the impact of small changes to them. From
information theory 32 bit floating point arithmetic is
dissipative and even with 1501 test cases deep
mutations seldom have any impact on fitness. Absolute
difference between parent and child evaluation can grow
as well as fall further from the code change location
but the number of disrupted fitness tests falls
monotonically. In many cases deeply nested expressions
are robust to crossover syntax changes, bugs, errors,
run time glitches, perturbations, etc., because their
disruption falls to zero, and so it fails to propagate
beyond the program.",
- }
Genetic Programming entries for
William B Langdon
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