Optimizing evolutionary CSG tree extraction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Friedrich:2019:GECCO,
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author = "Markus Friedrich and Pierre-Alain Fayolle and
Thomas Gabor and Claudia Linnhoff-Popien",
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title = "Optimizing evolutionary {CSG} tree extraction",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "1183--1191",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321771",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Hierarchical
representations, Shape modelling,3D Geometry
Processing, CAD, CSG, 3D-Reconstruction, Evolutionary
Algorithms",
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size = "9 pages",
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abstract = "The extraction of 3D models represented by
Constructive Solid Geometry (CSG) trees from point
clouds is a common problem in reverse engineering
pipelines as used by Computer Aided Design (CAD) tools.
We propose three independent enhancements on
state-of-the-art Genetic Algorithms (GAs) for CSG tree
extraction: (1) A deterministic point cloud filtering
mechanism that significantly reduces the computational
effort of objective function evaluations without loss
of geometric precision, (2) a graph-based partitioning
scheme that divides the problem domain in smaller parts
that can be solved separately and thus in parallel and
(3) a 2-level improvement procedure that combines a
recursive CSG tree redundancy removal technique with a
local search heuristic, which significantly improves GA
running times. We show in an extensive evaluation that
our optimized GA-based approach provides faster running
times and scales better with problem size compared to
state-of-the-art GA-based approaches.",
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notes = "Also known as \cite{3321771} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Markus Friedrich
Pierre-Alain Fayolle
Thomas Gabor
Claudia Linnhoff-Popien
Citations