Arpit Bhardwaj

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The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the(More)
The concept of “bloat” in Genetic Programming is a well established phenomenon characterized by variable-length genomes gradually increasing in size during evolution. Bloat is basically a problem that occurs during crossover and mutation. In this paper we are proposing a special type of crossover operation named as Fitness, Elitism, Depth(More)
In this paper, we present a new method for classification of electroencephalogram (EEG) signals using Genetic Programming (GP). The Empirical Mode Decomposition (EMD) is used to extract the features of EEG signals which served as an input for the GP. In this paper, new constructive crossover and mutation operations are also produced to improve GP. In these(More)
During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness'a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and(More)
A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the(More)
A Multiclass classifier is an approach for designing classifiers for a m-class (m>=2) problem using genetic programming (GP). In this paper we proposed three methods named Triple Tournament Method, Special Mutation Method and a Step Wise Crossover method. In Special Mutation technique we are generating the two child from single parent and selecting the one(More)