Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The effect multiple query representations on information retrieval system performance
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A network approach to probabilistic information retrieval
ACM Transactions on Information Systems (TOIS)
Journal of the American Society for Information Science
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing similarity using multi-query relevance feedback
Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Query optimisation using an improved genetic algorithm
Proceedings of the ninth international conference on Information and knowledge management
Genetic Approach to Query Space Exploration
Information Retrieval
Query Optimization in Information Retrieval Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
Information Processing and Management: an International Journal
Journal of Biomedical Informatics
Local search: A guide for the information retrieval practitioner
Information Processing and Management: an International Journal
Improving the learning of Boolean queries by means of a multiobjective IQBE evolutionary algorithm
Information Processing and Management: an International Journal
Structure of morphologically expanded queries: A genetic algorithm approach
Data & Knowledge Engineering
Improving query expansion with stemming terms: a new genetic algorithm approach
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Real-coded genetic algorithm and application in the automatic composing the test paper
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Inferring document utility via a decision-making based retrieval model
International Journal of Knowledge-based and Intelligent Engineering Systems
Using genetic algorithms for query reformulation
FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
Improving the ranking quality of medical image retrieval using a genetic feature selection method
Decision Support Systems
Automated query learning with Wikipedia and genetic programming
Artificial Intelligence
State-of-the-art review on relevance of genetic algorithm to internet web search
Applied Computational Intelligence and Soft Computing
An intelligent system for personalized information retrieval: a genetic approach
Proceedings of the International C* Conference on Computer Science and Software Engineering
| Hi-index | 0.00 |
Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model.