Optimizing evolutionary CSG tree extraction

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Friedrich:2019:GECCO,

• author = "Markus Friedrich and Pierre-Alain Fayolle and Thomas Gabor and Claudia Linnhoff-Popien",
• title = "Optimizing evolutionary {CSG} tree extraction",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2019",
• 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",
• isbn13 = "978-1-4503-6111-8",
• pages = "1183--1191",
• address = "Prague, Czech Republic",
• DOI = "doi:10.1145/3321707.3321771",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, Hierarchical representations, Shape modelling,3D Geometry Processing, CAD, CSG, 3D-Reconstruction, Evolutionary Algorithms",
• size = "9 pages",
• 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.",
• 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