- author = "Vic Ciesielski and Gayan Wijesinghe and Andrew Innes and Sabu John",
- title = "Analysis of the Superiority of Parameter Optimization over Genetic Programming for a Difficult Object Problem",
- booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary Computation",
- year = "2006",
- editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas",
- pages = "4407--4414",
- address = "Vancouver",
- month = "16-21 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-7803-9487-9",
-
DOI = "
doi:10.1109/CEC.2006.1688454",
- size = "8 pages",
- abstract = "We describe a progression of solutions to a difficult object detection problem, that of locating landmarks in X-Rays used in orthodontic treatment planning. In our first formulation an object detector was a genetic program whose inputs were a number of attributes computed from a scanning window. We used a rich function set comprising + - times divide min; max; ifthenelse. Experimentation with different function sets revealed that using the function set + - gave detectors that were almost as accurate. Such detectors are essentially a linear combination of attributes so we also implemented a parameter optimisation solution with a particle swarm optimiser. Contrary to expectation, the PSO detectors are more accurate and smaller than the GP ones. Our analysis of the reasons for this reveals that (1) the PSO approach involves a considerably smaller search space than the GP approach, (2) in the PSO approach there is a 1-1 mapping between genotype and phenotype while in the GP approach this mapping is many-1 and many semantically equivalent potential solutions are evaluated, (3) the fitness landscape for PSO is a good one for search in that solutions are distributed in areas of high fitness that are easy to locate while the GP landscape is much more difficult to characterise and areas of high fitness more difficult to find.",
-
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages = {"}1264--1271{"},",
