Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the seventh international conference (1990) on Machine learning
Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
GANNET: a genetic algorithm for searching topology and weight spaces in neural network design. The first step in finding a neural network solution
Copy detection mechanisms for digital documents
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
CHECK: a document plagiarism detection system
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Signature extraction for overlap detection in documents
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Evolving neural networks through augmenting topologies
Evolutionary Computation
Bayesian Artificial Intelligence: Kevin B. Korb, Ann E. Nicholson, Chapman & Hall, 2004, 354 pages
Pattern Analysis & Applications
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Intrinsic plagiarism detection
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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Intrinsic Plagiarism Detection attempts to identify portions of a document which have been plagiarised without the use of reference collections. This is typically achieved by developing a classifier using support vector machines or hand-crafted neural networks. This paper presents an evolutionary neural network approach to the development of an intrinsic plagiarism detection classifier which is capable of evolving both the weights and structure of a neural network. The neural network is empirically tested on a corpus of documents and is shown to perform well.