Self-stabilizing systems in spite of distributed control
Communications of the ACM
Maximal matching stabilizes in time O(m)
Information Processing Letters
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Self-Stabilizing Protocols for Maximal Matching and Maximal Independent Sets for Ad Hoc Networks
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Linear time self-stabilizing colorings
Information Processing Letters
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics
Combinatorics, Probability and Computing
Distributed approximation: a survey
ACM SIGACT News
A self-stabilizing algorithm for coloring planar graphs
Distributed Computing - Special issue: Self-stabilization
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Graph Treewidth and Geometric Thickness Parameters
Discrete & Computational Geometry
Self-stabilizing coloration in anonymous planar networks
Information Processing Letters
A new self-stabilizing maximal matching algorithm
SIROCCO'07 Proceedings of the 14th international conference on Structural information and communication complexity
Self-stabilizing algorithms for graph coloring with improved performance guarantees
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Consider a synchronized distributed system where each node can only observe the state of its neighbors. Such a system is called self-stabilizing if it reaches a stable global state in a finite number of rounds. Allowing two different states for each node induces a cut in the graph. In each round, every node decides whether it is (locally) satisfied with the current cut. Afterwards all unsatisfied nodes change sides independently with a fixed probability p. Using different notions of satisfaction enables the computation of maximal and minimal cuts, respectively. We analyze the expected time until such cuts are reached on several graph classes and study the impact of the parameter p and the initial cut.