23,583 research outputs found

    Qualitative Properties of alpha-Weighted Scheduling Policies

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    We consider a switched network, a fairly general constrained queueing network model that has been used successfully to model the detailed packet-level dynamics in communication networks, such as input-queued switches and wireless networks. The main operational issue in this model is that of deciding which queues to serve, subject to certain constraints. In this paper, we study qualitative performance properties of the well known α\alpha-weighted scheduling policies. The stability, in the sense of positive recurrence, of these policies has been well understood. We establish exponential upper bounds on the tail of the steady-state distribution of the backlog. Along the way, we prove finiteness of the expected steady-state backlog when α<1\alpha<1, a property that was known only for α1\alpha\geq 1. Finally, we analyze the excursions of the maximum backlog over a finite time horizon for α1\alpha \geq 1. As a consequence, for α1\alpha \geq 1, we establish the full state space collapse property.Comment: 13 page

    Bounding Bloat in Genetic Programming

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    While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations. A naturally occurring problem is that of bloat (unnecessary growth of solutions) slowing down optimization. Theoretical analyses could so far not bound bloat and required explicit assumptions on the magnitude of bloat. In this paper we analyze bloat in mutation-based genetic programming for the two test functions ORDER and MAJORITY. We overcome previous assumptions on the magnitude of bloat and give matching or close-to-matching upper and lower bounds for the expected optimization time. In particular, we show that the (1+1) GP takes (i) Θ(Tinit+nlogn)\Theta(T_{init} + n \log n) iterations with bloat control on ORDER as well as MAJORITY; and (ii) O(TinitlogTinit+n(logn)3)O(T_{init} \log T_{init} + n (\log n)^3) and Ω(Tinit+nlogn)\Omega(T_{init} + n \log n) (and Ω(TinitlogTinit)\Omega(T_{init} \log T_{init}) for n=1n=1) iterations without bloat control on MAJORITY.Comment: An extended abstract has been published at GECCO 201
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