Semantic Backpropagation for Designing Search Operators in Genetic Programming
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- @Article{Pawlak:2014:ieeeEC,
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author = "Tomasz P. Pawlak and Bartosz Wieloch and
Krzysztof Krawiec",
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title = "Semantic Backpropagation for Designing Search
Operators in Genetic Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2015",
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volume = "19",
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number = "3",
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pages = "326--340",
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month = jun,
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keywords = "genetic algorithms, genetic programming, program
synthesis, semantics, reversible computing, problem
decomposition, mutation, geometric crossover",
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ISSN = "1089-778X",
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URL = "http://www.cs.put.poznan.pl/tpawlak/?Semantic%20Backpropagation%20for%20Designing%20Search%20Operators%20in%20Genetic%20Programming,16",
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appendix_url = "http://www.cs.put.poznan.pl/tpawlak/files/research/2013SemanticBackpropagation/2013IEEE_Appendix.pdf",
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URL = "http://dx.doi.org/10.1109/TEVC.2014.2321259",
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DOI = "doi:10.1109/TEVC.2014.2321259",
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code_url = "http://www.cs.put.poznan.pl/tpawlak/files/research/2013SemanticBackpropagation/Evolution-src.zip",
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abstract = "In genetic programming, a search algorithm is expected
to produce a program that achieves the desired final
computation state (desired output). To reach that
state, an executing program needs to traverse certain
intermediate computation states. An evolutionary search
process is expected to autonomously discover such
states. This can be difficult for nontrivial tasks that
require long programs to be solved. The semantic
back-propagation algorithm proposed in this paper
heuristically inverts the execution of evolving
programs to determine the desired intermediate
computation states. Two search operators, Random
Desired Operator and Approximately Geometric Semantic
Crossover, use the intermediate states determined by
semantic backpropagation to define subtasks of the
original programming task, which are then solved using
an exhaustive search. The operators outperform the
standard genetic search operators and other
semantic-aware operators when compared on a suite of
symbolic regression and Boolean benchmarks. This result
and additional analysis conducted in this study
indicate that semantic back propagation helps evolution
at identifying the desired intermediate computation
states, and makes the search process more efficient.",
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notes = "Java source code
http://www.cs.put.poznan.pl/tpawlak/files/research/2013SemanticBackpropagation/Evolution-src.zip
Also known as \cite{6808504}",
- }
Genetic Programming entries for
Tomasz Pawlak
Bartosz Wieloch
Krzysztof Krawiec
Citations