Informatics Publishing Limited

P. Vivekanandan ¹, R. Nedunchezhian ²

Affiliations
1 Department of CSE, Park College of Engineering and Technology, Coimbatore, IN
2 Department of CSE, Kalaignar Karunanidhi Institute of Technology, Coimbatore, IN

Abstract

Genetic algorithm is a search technique purely based on natural evolution process.  It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is particularly difficult to apply GA when the training data set becomes too large and not fully available. An   incremental Genetic algorithm based on boosting phenomenon is proposed in this paper which constructs a weak ensemble of classifiers in a fast incremental manner and thus tries to reduce the learning cost considerably.

Keywords

Classification, Incremental Learning, Genetic Algorithm (Ga), Scalability, Boosting.

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N. Prabakaran ¹, P. Vivekanandan ²

Affiliations
1 Department of Computer Applications, Rajalakshmi Engineering College, Chennai-602105, IN
2 Knowledge Data Centre, Anna University, Chennai-600025, IN

Abstract

We can generate a secret key using neural cryptography, which is based on synchronization of Tree Parity Machines (TPMs) by mutual learning. In the proposed TPMs random inputs are replaced with queries which are considered. The queries depend on the current state of A and B TPMs. Then, TPMs hidden layer of each output vectors are compared. That is, the output vectors of hidden unit using Hebbian learning rule, left-dynamic hidden unit using Random walk learning rule and right-dynamic hidden unit using Anti-Hebbian learning rule are compared. Among the compared values, one of the best values is received by the output layer. The queries fix the security against majority flipping and geometric attacks are shown in this paper. The new parameter H can accomplish a higher level of security for the neural key-exchange protocol without altering the average synchronization time.

Keywords

Majority Attacks, Neural Synchronization, Queries, Tree Parity Machines.

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