Labeled point pattern matching by Delaunay triangulation and maximal cliques
Pattern Recognition
List organizing strategies using stochastic move-to-front and stochastic move-to-rear operations
SIAM Journal on Computing
Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Learning automata: an introduction
Learning automata: an introduction
Improvements to an Algorithm for Equipartitioning
IEEE Transactions on Computers
The priority-based coloring approach to register allocation
ACM Transactions on Programming Languages and Systems (TOPLAS)
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combinatorial optimization in system configuration design
Automation and Remote Control
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The graph coloring problem (GCP) is a widely studied combinatorial optimization problem with numerous applications, including time tabling, frequency assignment, and register allocation. The growing need for more efficient algorithms has led to the development of several GCP solvers. In this paper, we introduce the first GCP solver that is based on Learning Automata (LA). We enhance traditional Random Walk with LA-based learning capability, encoding the GCP as a Boolean satisfiability problem (SAT). Extensive experiments demonstrate that the LA significantly improve the performance of RW, thus laying the foundation for novel LA-based solutions to the GCP.