Principles of artificial intelligence
Principles of artificial intelligence
Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Learning automata: an introduction
Learning automata: an introduction
Genetic algorithms: foundations and applications
Annals of Operations Research
Using genetic algorithms to solve NP-complete problems
Proceedings of the third international conference on Genetic algorithms
Abstract heuristic search methods for graph partitioning
Abstract heuristic search methods for graph partitioning
Breaking Substitution Cyphers Using Stochastic Automata
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
String taxonomy using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A learning approach to processor allocation in parallel systems
Proceedings of the eighth international conference on Information and knowledge management
Call Admission Control in Cellular Mobile Networks: A Learning Automata Approach
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
A Learning Automata Based Dynamic Guard Channel Scheme
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
A formal analysis of why heuristic functions work
Artificial Intelligence
Finding optimal solutions to the graph partitioning problem with heuristic search
Annals of Mathematics and Artificial Intelligence
An Efficient Dynamic Algorithm for Maintaining All-Pairs Shortest Paths in Stochastic Networks
IEEE Transactions on Computers
IEEE Transactions on Computers
Learning automata based classifier
Pattern Recognition Letters
Brief paper: Asynchronous cellular learning automata
Automatica (Journal of IFAC)
Skin segmentation based on cellular learning automata
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
International Journal of Systems Science
A formal analysis of why heuristic functions work
Artificial Intelligence
An adaptive call admission algorithm for cellular networks
Computers and Electrical Engineering
Modeling a student-classroom interaction in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving graph coloring problems using learning automata
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Combining finite learning automata with GSAT for the satisfiability problem
Engineering Applications of Artificial Intelligence
Modeling a student's behavior in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling a domain in a tutorial-like system using learning automata
Acta Cybernetica
Learning automata based dynamic guard channel algorithms
Computers and Electrical Engineering
Service selection in stochastic environments: a learning-automaton based solution
Applied Intelligence
Discretized bayesian pursuit --- a new scheme for reinforcement learning
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
Stochastic Learning for SAT-Encoded Graph Coloring Problems
International Journal of Applied Metaheuristic Computing
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Given a graph G, we intend to partition its nodes into two sets of equal size so as to minimize the sum of the cost of the edges having end-points in different sets. This problem, called the uniform graph partitioning problem, is known to be NP-Complete. In this paper we propose the first reported learning-automaton based solution to the problem. We compare this new solution to various reported schemes such as the Kernighan-Lin's algorithm, and two excellent recent heuristic methods proposed by Rolland et al.驴an extended local search algorithm and a genetic algorithm. The current automaton-based algorithm outperforms all the other schemes. We believe that it is the fastest algorithm reported to date. Additionally, our solution can also be adapted for the GPP (See note at end of Section 1) in which the edge costs are not constant but random variables whose distributions are unknown.