| dc.contributor.author | Newaz, S.M Saif | |
| dc.contributor.author | Pieta, Maliha Anjum | |
| dc.contributor.author | Ahmed, Mehreen | |
| dc.date.accessioned | 2016-01-19T13:09:33Z | |
| dc.date.available | 2016-01-19T13:09:33Z | |
| dc.date.issued | 2015-12 | |
| dc.identifier.other | ID 10201028 | |
| dc.identifier.other | ID 09201009 | |
| dc.identifier.other | ID 11201010 | |
| dc.identifier.uri | http://hdl.handle.net/10361/4892 | |
| dc.description | This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015. | en_US |
| dc.description.abstract | In this paper we propose a ranking algorithm, HybridABC that is built on swarm based
algorithm. In our proposed HybridABC algorithm we merged Artificial Bee Colony (ABC)
algorithm with Differential Evolution (DE) algorithm. The ABC is a swarm-based metaheuristic
algorithm inspired by the intelligent foraging pattern of bees and Differential
Evolution is a population-based stochastic search technique. The proposed implementation of
ABC has been tested using the LETOR dataset, which is a standard benchmark dataset for
evaluating ranking functions. Our results display that our proposed HybridABC can compete
and in many cases more efficient than other state-of-the-art algorithm proposed in ranking
web pages based on Genetic Algorithm (GA). | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.subject | Computer science and engineering | en_US |
| dc.subject | HybridABC | en_US |
| dc.title | Learning a ranking function for information retrieval using HybridABC | en_US |
| dc.type | Thesis | en_US |
