- title = "Evolvable {3D} Modeling for Model-Based Object Recognition Systems",
- author = "Thang Nguyen and Thomas Huang",
- booktitle = "Advances in Genetic Programming",
- publisher = "MIT Press",
- editor = "Kenneth E. {Kinnear, Jr.}",
- year = "1994",
- chapter = "22",
- pages = "459--475",
- size = "17 pages",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
-
URL = "
http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap22.pdf",
- abstract = "This paper presents a system that evolves 3D models over time, eventually producing novel models that are more desirable than initial models. The algorithm starts with some crude models given by the user, or randomly-generated models from a given model-grammar with generic design rules and loose constraints. The underlying philosophy here is of gradually evolving the initial models into better models over many generations. There is a close analog in the evolution of species where better-fit species gradually emerge and form specialised niches, a highly efficient process of complex structural and functional optimization. Our simulation results for 3D jet aircraft model design illustrate that this approach to model design and refinement is feasible and effective. The intended application domain is for automatic object recognition system, though the model fitness criteria is currently determined by user interactive selection.",
-
notes = "GP on very small populations (10). Very powefull,
aircraft design, primatives
see also http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/gp-3D-modeling.ps.Z \cite{nguyen:emsat3d}",
