Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Neural computing: theory and practice
Neural computing: theory and practice
An O(n log n+m log log n) maximum weight clique algorithm for circular-arc graphs
Information Processing Letters
Finding a maximum independent set in a permutation graph
Information Processing Letters
Optimization Using Neural Networks
IEEE Transactions on Computers - Special issue on artificial neural networks
Finding a maximum set of independent chords in a circle
Information Processing Letters
Optimal parallel time bounds for the maximum clique problem on intervals
Information Processing Letters
Information Processing Letters
A Hopfield network algorithm for the bipartite subgraph problem
ICYCS'93 Proceedings of the third international conference on Young computer scientists
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Repair of RAMs With Clustered Faults
ICCD '92 Proceedings of the 1991 IEEE International Conference on Computer Design on VLSI in Computer & Processors
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Parallelism in binary hopfield networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
An improved simulated annealing algorithm for the maximum independent set problem
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
| Hi-index | 14.98 |
This paper presents an efficient technique to map the minimum vertex cover and two closely related problems (maximum independent set and maximum clique) onto the Hopfield neural networks. The proposed approach can be used to find near-optimum solutions for these problems in parallel, and particularly the network algorithm always yields minimal vertex covers. A systematic way of deriving energy functions is described. Based on these relationships, other NP-complete problems in graph theory can also be solved by neural networks. Extensive simulations were performed, and the experimental results show that the network algorithm outperforms the well-known greedy algorithm for vertex cover problems.