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Graph Partitioning Using Learning Automata (1994)
| Citations: | 3 - 0 self |
BibTeX
@MISC{Oommen94graphpartitioning,
author = {B. John Oommen and Edward V. De St. Croix},
title = {Graph Partitioning Using Learning Automata},
year = {1994}
}
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Abstract
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 (GPP), 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. -- 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 1 in which the edge costs are not constant but random variables whose distributions are unknown. Keywords : Heuristic search; Graph partitioning; Adaptive learning; Learning Automata. I. INTRODUCTION Let G = (V, E) be...
Keyphrases
graph partitioning using learning automaton graph partitioning uniform graph partitioning problem edge cost different set genetic algorithm extended local search algorithm new solution adaptive learning introduction let learning automaton random variable equal size excellent recent heuristic method heuristic search current automaton-based algorithm
