A Hybrid Approach to the Maximum Clique Problem in the Domain of Information Management

In 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...

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Published inProceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015) Vol. 415; pp. 323 - 336
Main Authors Alexander, Demidovskij, Eduard, Babkin, Babkina, Tatiana
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesAdvances in Intelligent Systems and Computing
Subjects
Artificial intelligence
Genetic algorithm
Hopfield neural network
Information management
Mathematical modelling
Maximum clique problem
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Summary:In 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.
ISBN:9783319272115
331927211X
ISSN:2194-5357
2194-5365
DOI:10.1007/978-3-319-27212-2_25