Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Information Retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automated shape composition based on cell biology and distributed genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Parallel learning to rank for information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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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.