- title = "Learning to rank using evolutionary computation: immune programming or genetic programming?",
- author = "Shuaiqiang Wang and Jun Ma and Jiming Liu",
- booktitle = "Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009",
- year = "2009",
- editor = "David Wai-Lok Cheung and Il-Yeol Song and Wesley W. Chu and Xiaohua Hu and Jimmy J. Lin",
- pages = "1879--1882",
- address = "Hong Kong",
- month = nov # " 2-6",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, Poster session 6",
- isbn13 = "978-1-60558-512-3",
-
DOI = "
doi:10.1145/1645953.1646254",
- bibdate = "2009-11-17",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/cikm/cikm2009.html#WangML09",
-
abstract = "Nowadays ranking function discovery approaches using
Evolutionary Computation (EC), especially Genetic
Programming (GP), have become an important branch in
the Learning to Rank for Information Retrieval (LR4IR)
field. Inspired by the GP based learning to rank
approaches, we provide a series of generalized
definitions and a common framework for the application
of EC in learning to rank research. Besides, according
to the introduced framework, we propose RankIP, a
ranking function discovery approach using Immune
Programming (IP). Experimental results demonstrate that
RankIP evidently outperforms the baselines.
In addition, we study the differences between IP and GP in theory and experiments. Results show that IP is more suitable for LR4IR due to its high diversity.",
