The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper we have used CGP (Cartesian Genetic Programming) to obtain an optimum polymorphic circuit for speedy recognition of hand written characters. Images are converted to binary form and arranged in one-dimension array. This data is fed, in parallel, to the system. Experiments have demonstrated that promising results can be obtained using a much simpler circuit having 1-bit multiplexers. All...
Load forecasting has been an inevitable issue in electric power supply in past. It is always desired to predict the load requirements in order to generate and supply electric power efficiently. In this research, a neuro-evolutionary technique known as Cartesian Genetic Algorithm evolved Artificial Neural Network (CGPANN) has been deployed to develop a peak load forecasting model for the prediction...
Biological brains are capable of general learning without supervision. This is learning across multiple domains without interference. Unlike artificial neural networks, in real brains, learned information is not purely encoded in real-valued weights but instead it resides in many neural aspects. Such aspects include, dendritic and axonal morphology, number and location of synapses, synaptic strengths...
A novel Neuroevolutionary technique based on Cartesian Genetic Programming is proposed (CGPANN). ANNs are encoded and evolved using a representation adapted from the CGP. We have tested the new approach on the single pole balancing problem. Results show that CGPANN evolves solutions faster and of higher quality than the most powerful algorithms of Neuroevolution in the literature.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.