- author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
- title = "Looseness Controlled Crossover in GP for Object Recognition",
- 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 = "4428--4435",
- address = "Vancouver",
- month = "16-21 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-7803-9487-9",
-
DOI = "
doi:10.1109/CEC.2006.1688457",
- size = "8 pages",
- abstract = "improving the crossover operator in genetic programming for object recognition particularly object classification problems. In this approach, instead of randomly choosing the crossover points as in the standard crossover operator, we use a measure called looseness to guide the selection of crossover points. Rather than using the genetic beam search only, this approach uses a hybrid beam-hill climbing search scheme in the evolutionary process. This approach is examined and compared with the standard crossover operator and the headless chicken crossover method on a sequence of object classification problems. The results suggest that this approach outperforms both the headless chicken crossover and the standard crossover on all of these problems.",
-
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
