A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
A novel method for extracting knowledge from neural networks with evolving SQL queries
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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This paper presents a methodology for applying the principles of evolutionary computation to knowledge discovery in databases by evolving SQL queries that describe datasets. In our system, the fittest queries are rewarded by having their attributes being given a higher probability of surviving in subsequent queries. The advantages of using SQL queries include their readability for non-experts and ease of integration with existing databases. The evolutionary algorithm (EA) used in our system is very different from existing EAs, but seems to be effective and efficient according to the experiments to date with three different testing data sets.