Balls and bins: a study in negative dependence
Random Structures & Algorithms
Wiener index versus maximum degree in trees
Discrete Applied Mathematics
Near-optimal network design with selfish agents
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Proceedings of the twenty-second annual symposium on Principles of distributed computing
The wiener index of simply generated random trees
Random Structures & Algorithms
Evolutionary algorithms for the self-organized evolution of networks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Algorithms for selfish agents mechanism design for distributed computation
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Modeling and Designing Real---World Networks
Algorithmics of Large and Complex Networks
The emergence of sparse spanners and greedy well-separated pair decomposition
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
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The modeling and analysis of large networks of autonomous agents is an important topic with applications in many different disciplines. One way of modeling the development of such networks is by means of an evolutionary process. The autonomous agents are randomly chosen to become active, may apply some kind of local mutation operators to the network and decide about accepting these changes via some fitness-based selection whereas the fitness models the agent's preferences. This general framework for the self-organized evolution of networks can be instantiated in many different ways. For interesting instances, one would like to know whether stable topologies eventually evolve and how long this process may take. Here, known results for one instantiation are improved. Moreover, a more natural and local instantiation is presented and analyzed with respect to the expected time needed to reach a stable state.