Optimal 2D Model Matching Using a Messy Genetic Algorithm

A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which chromosome length can vary. Consequently, population dynamics can be made to drive a relatively efficient and robust search for larger and better matches. Run-times for the Messy GA are as much as an order of magnitude smaller than for random starts local search. When compared to a faster Key-Feature Algorithm, the Messy Genetic Algorithm more reliably finds optimal matches. Empirical results are presented for both controlled synthetic and real world line matching problems.

• Type: Conference
• Year: 1998
• Where: AAAI
• Authors: J. Ross Beveridge
• Keywords: AAAI 1998, Intelligent Agents, Messy GA, Messy Genetic Algorithm, Standard Genetic Algorithm