Abstract

AbstractIn this paper we observe the opportunity to offer new methods of solving NP-hard problems which frequently arise in the domain of information management, including design of database structures and big data processing. In our research we are focusing on the Maximum Clique Problem (MCP) and propose a new approach to solving that problem. The approach combines the artificial neuro-network paradigm and genetic programming. For boosting the convergence of the Hopfield Neural Network (HNN) we propose the genetic algorithm as the selection mechanism for terms of energy function. As a result, we demonstrate the proposed approach on experimental graphs and formulate two hypotheses for further research.KeywordsInformation managementMaximum clique problemHopfield neural networkGenetic algorithm

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.