Optimal determination of user-oriented clusters: an application for the reproductive plan
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
On the allocation of documents in multiprocessor information retrieval systems
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Search improvement via automatic query reformulation
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Computer
An introduction to genetic algorithms
An introduction to genetic algorithms
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
An intelligent personal spider (agent) for dynamic Internet/intranet searching
Decision Support Systems - Special issue: intranets and intranetworking
A fuzzy genetic algorithm approach to an adaptive information retrieval agent
Journal of the American Society for Information Science
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
SUITOR: an attentive information system
Proceedings of the 5th international conference on Intelligent user interfaces
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modern Information Retrieval
Web Search Using a Genetic Algorithm
IEEE Internet Computing
Query Optimization in Information Retrieval Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
The use of dynamic contexts to improve casual internet searching
ACM Transactions on Information Systems (TOIS)
Google Hacks: 100 Industrial-Strength Tips and Tools
Google Hacks: 100 Industrial-Strength Tips and Tools
Aiding knowledge capture by searching for extensions of knowledge models
Proceedings of the 2nd international conference on Knowledge capture
Improving search results with data mining in a thematic search engine
Computers and Operations Research
Topical web crawlers: Evaluating adaptive algorithms
ACM Transactions on Internet Technology (TOIT)
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Suggesting novel but related topics: towards context-based support for knowledge model extension
Proceedings of the 10th international conference on Intelligent user interfaces
Downloading textual hidden web content through keyword queries
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
A generic construct based workload model for web search
Information Processing and Management: an International Journal
Multi-objective Query Optimization Using Topic Ontologies
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering 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
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Systems for searching the Web based on thematic contexts can be built on top of a conventional search engine and benefit from the huge amount of content as well as from the functionality available through the search engine interface. The quality of the material collected by such systems is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Some characteristics of this optimization problem are: (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, (3) the possibility of finding multiple solutions, and in many cases (4) the quest for novelty. This article describes optimization techniques based on Genetic Algorithms to evolve ''good query terms'' in the context of a given topic. The proposed techniques place emphasis on searching for novel material that is related to the search context. We discuss the use of a mutation pool to allow the generation of queries with new terms, study the effect of different mutation rates on the exploration of query-space, and discuss the use of a especially developed fitness function that favors the construction of queries containing novel but related terms.