StGA: An application of a genetic algorithm to stochastic learning automata

1996 ◽  
Vol 27 (10) ◽  
pp. 68-78 ◽  
Author(s):  
Masaharu Munetomi ◽  
Yoshiaki Takai ◽  
Yoshiharu Sato
Keyword(s):  
Genetic Algorithm ◽  
Learning Automata ◽  
Stochastic Learning

Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata

1986 ◽  
pp. 235-246 ◽  
Author(s):  
Andrew G. Barto ◽  
P. Anandan ◽  
Charles W. Anderson
Keyword(s):  
Learning Automata ◽  
Stochastic Learning

Stochastic Learning Automata (SLA)

2018 ◽  
pp. 5-24
Author(s):  
Phil Mars ◽  
J.R. Chen ◽  
Raghu Nambiar
Keyword(s):  
Learning Automata ◽  
Stochastic Learning

Stochastic Learning for SAT- Encoded Graph Coloring Problems

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Noureddine Bouhmala ◽  
Ole-Christoffer Granmo
Keyword(s):  
Random Walk ◽  
Graph Coloring ◽  
Learning Automata ◽  
Frequency Assignment ◽  
Random Walk Algorithm ◽  
Stochastic Learning ◽  
Graph Coloring Problem ◽  
Sat Encoding ◽  
Test Sets ◽  
Walk Algorithm

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

Stochastic Learning for SAT- Encoded Graph Coloring Problems

2012 ◽  
pp. 306-325
Author(s):  
Noureddine Bouhmala ◽  
Ole-Christoffer Granmo
Keyword(s):  
Random Walk ◽  
Graph Coloring ◽  
Learning Automata ◽  
Combinatorial Optimization Problem ◽  
Frequency Assignment ◽  
Stochastic Learning ◽  
Graph Coloring Problem ◽  
Sat Encoding ◽  
Test Sets ◽  
Walk Algorithm

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

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