Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Query modification using genetic algorithms in vector space models
International Journal of Expert Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Journal of the American Society for Information Science
Crossover improvement for the genetic algorithm in information retrieval
Information Processing and Management: an International Journal
A fuzzy genetic algorithm approach to an adaptive information retrieval agent
Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
Journal of the American Society for Information Science and Technology
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Information Retrieval
Modern Information Retrieval
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Genetic Approach to Query Space Exploration
Information Retrieval
A test of genetic algorithms in relevance feedback
Information Processing and Management: an International Journal
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiple query evaluation based on an enhanced genetic algorithm
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Order-based fitness functions for genetic algorithms applied to relevance feedback
Journal of the American Society for Information Science and Technology
On using genetic algorithms for multimodal relevance optimization in information retrieval
Journal of the American Society for Information Science and Technology
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Local search: A guide for the information retrieval practitioner
Information Processing and Management: an International Journal
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
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The Inductive Query By Example (IQBE) paradigm allows a system to automatically derive queries for a specific Information Retrieval System (IRS). Classic IRSs based on this paradigm [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] generate a single solution (Boolean query) in each run, that with the best fitness value, which is usually based on a weighted combination of the basic performance criteria, precision and recall.A desirable aspect of IRSs, especially of those based on the IQBE paradigm, is to be able to get more than one query for the same information needs, with high precision arid recall values or with different trade-offs between both.In this contribution, a new IQBE process is proposed combining a previous basic algorithm to automatically derive Boolean queries for Boolean IRSs [Smith, M., & Smith, M. (1997). The use of genetic programming to build Boolean queries for text retrieval through relevance feedback. Journal of Information Science, 23(6), 423-431] and an advanced evolutionary multiobjective approach [Coello, C. A., Van Veldhuizen, D. A., & Lamant, G.B. (2002). Evolutionary algorithms for solving multiobjective problems. Kluwer Academic Publishers], which obtains several queries with a different precision-recall trade-off in a single run. The performance of the new proposal will be tested on the Cranfield and CACM collections and compared to the well-known Smith and Smith's algorithm, showing how it improves the learning of queries and thus it could better assist the user in the query formulation process.