A Multiplicity-Preserving Crossover Operator on Graphs
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Thoelke:2022:MODELS,
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author = "Henri Thoelke and Jens Kosiol",
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title = "A Multiplicity-Preserving Crossover Operator on
Graphs",
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booktitle = "Proceedings of the 25th International Conference on
Model Driven Engineering Languages and Systems:
Companion Proceedings",
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year = "2022",
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pages = "588--597",
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address = "Montreal, Quebec, Canada",
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publisher = "Association for Computing Machinery",
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keywords = "genetic algorithms, genetic programming, search-based
software engineering, SBSE, evolutionary algorithms,
crossover, model-driven optimization, MDO,
consistency-preservation",
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isbn13 = "9781450394673",
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URL = "https://doi.org/10.1145/3550356.3561587",
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DOI = "doi:10.1145/3550356.3561587",
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size = "10 pages",
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abstract = "Evolutionary algorithms usually explore a search space
of solutions by means of crossover and mutation. While
a mutation consists of a small, local modification of a
solution, crossover mixes the genetic information of
two solutions to compute a new one. For model-driven
optimization (MDO), where models directly serve as
possible solutions (instead of first transforming them
into another representation), only recently a generic
crossover operator has been developed. Using graphs as
a formal foundation for models, we further refine this
operator in such a way that additional well-formedness
constraints are preserved: We prove that, given two
models that satisfy a given set of multiplicity
constraints as input, our refined crossover operator
computes two new models as output that also satisfy the
set of constraints.",
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notes = "is this GP?
See https://arxiv.org/pdf/2208.10881.pdf for Extended
version
Philipps-Universitaet Marburg Marburg, Germany",
- }
Genetic Programming entries for
Henri Thoelke
Jens Kosiol
Citations