Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several...
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Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Levy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems. (c) 2006 Elsevier Inc. All rights reserved.
The longest increasing circular subsequence (LICS) of a list is considered. A Monte Carlo algorithm to compute it is given which has worst case execution time 0(n(3/2) log n) and storage requirement 0(n). It is proved...
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The longest increasing circular subsequence (LICS) of a list is considered. A Monte Carlo algorithm to compute it is given which has worst case execution time 0(n(3/2) log n) and storage requirement 0(n). It is proved that the expected length mu(n) of the LICS satisfies lim(n ->infinity) mu(n)/2 root = 1. Numerical experiments with the algorithm suggest that vertical bar mu(n) - 2 root n vertical bar = O(n(1/6)). (c) 2006 Elsevier B.V. All rights reserved.
Virtual private network design is the following NP-hard problem. We are given a communication network represented as a weighted graph with thresholds on the nodes which represent the amount of flow that a node can sen...
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Virtual private network design is the following NP-hard problem. We are given a communication network represented as a weighted graph with thresholds on the nodes which represent the amount of flow that a node can send to and receive from the network. The task is to reserve capacities at minimum cost and to specify paths between every ordered pair of nodes such that all valid traffic-matrices can be routed along the corresponding paths. Recently, this network design problem has received considerable attention in the literature. It is motivated by the fact that the exact amount of flow which is exchanged between terminals is not known in advance and prediction is often elusive. The main contributions of this paper are as follows: (1) Using Hu's 2-commodity flow theorem, we provide a new and considerably stronger lower bound on the cost of an optimum solution. With this lower bound we reanalyze a simple routing scheme which has been described in the literature many times, and provide an improved upper bound on its approximation ratio. (2) We present a new randomized approximation algorithm. In contrast to earlier approaches from the literature, the resulting solution does not have tree structure. A combination of our new algorithm with the simple routing scheme yields an expected performance ratio of 3.79 for virtual private network design. This is a considerable improvement of the previously best known 5.55-approximation result [ A. Gupta, A. Kumar, and T. Roughgarden, Simpler and better approximation algorithms for network design, in Proceedings of the ACM Symposium on Theory of Computing, ACM, New York, 2003, pp. 365 -372]. (3) Our VPND algorithm uses a Steiner tree approximation algorithm as a subroutine. It is known that an optimum Steiner tree can be computed in polynomial time if the number of terminals is logarithmic. Replacing the approximate Steiner tree computation with an exact one whenever the number of terminals is sufficiently small, we finally reduce t
We present two new algorithms, Arc Length and Peer Count, for choosing a peer uniformly at random from the set of all peers in Chord (Proceedings of the ACM SIGCOMM 2001 Technical Conference, 2001). We show analytical...
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We present two new algorithms, Arc Length and Peer Count, for choosing a peer uniformly at random from the set of all peers in Chord (Proceedings of the ACM SIGCOMM 2001 Technical Conference, 2001). We show analytically that, in expectation, both algorithms have latency O(log n) and send O(log n) messages. Moreover, we show empirically that the average latency and message cost of Arc Length is 10.01log n and that the average latency and message cost of Peer Count is 20.02log n. To the best of our knowledge, these two algorithms are the first fully distributed algorithms for choosing a peer uniformly at random from the set of all peers in a Distributed Hash Table (DHT). Our motivation for studying this problem is threefold: to enable data collection by statistically rigorous sampling methods;to provide support for randomized, distributed algorithms over peer-to-peer networks;and to support the creation and maintenance of random links, and thereby offer a simple means of improving fault-tolerance.
We develop approximate counting of sets definable by Boolean circuits in bounded arithmetic using the dual weak pigeonhole principle (dWPHP(PV)), as a generalization of results from [15]. We discuss applications to fo...
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We develop approximate counting of sets definable by Boolean circuits in bounded arithmetic using the dual weak pigeonhole principle (dWPHP(PV)), as a generalization of results from [15]. We discuss applications to formalization of randomized complexity classes (such as BPP,APP, MA, AM) in PV1 + dWPHP(PV).
Programs written in C and C++ are susceptible to memory errors, including buffer overflows and dangling pointers. These errors, which can lead to crashes, erroneous execution, and security vulnerabilities, are notorio...
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ISBN:
(纸本)9781595936332
Programs written in C and C++ are susceptible to memory errors, including buffer overflows and dangling pointers. These errors, which can lead to crashes, erroneous execution, and security vulnerabilities, are notoriously costly to repair. Tracking down their location in the source code is difficult, even when the full memory state of the program is available. Once the errors are finally found, fixing them remains challenging: even for critical security-sensitive bugs, the average time between initial reports and the issuance of a patch is nearly one month. We present Exterminator, a system that automatically corrects heap-based memory errors without programmer intervention. Exterminator exploits randomization to pinpoint errors with high precision. From this information, Exterminator derives runtime patches that fix these errors both in current and subsequent executions. In addition, Exterminator enables collaborative bug correction by merging patches generated by multiple users. We present analytical and empirical results that demonstrate Exterminator's effectiveness at detecting and correcting both injected and real faults.
We study the completion time of distributed broadcast protocols in dynamic radio networks. The dynamic network is modelled by means of adversaries: we consider two of them that somewhat axe the extremal cases. We firs...
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ISBN:
(纸本)9781595936165
We study the completion time of distributed broadcast protocols in dynamic radio networks. The dynamic network is modelled by means of adversaries: we consider two of them that somewhat axe the extremal cases. We first analyze the weakest one, i.e., an oblivious, memoryless random adversary. At each time slot t, a graph G(t) is selected according to the well-known random graph model G(n,p). We derive a randomized protocol that, on input n and p, completes broadcasting in O(log n) time. Then, we prove that any randomized protocol has Omega(logn) completion time. When p is unknown, we present air oblivious homogeneous version of the Bar Yehuda-Goldreich-Itai's randomized protocol having O(log(2) n) completion time and we prove a lower bound Omega(log(2) n/log log n) that holds for any randomized oblivious homogeneous protocol. We emphasize that the above (poly-)logarithmic upper bounds also hold when random graphs are sparse and disconnected, i.e., for p = o(ln n/n). We then consider the deterministic worst-case adversary that, at each time slot, can make any network change (thus the strongest adversary). Up to now, it is not even known whether finite expected completion time is achievable against this adversary. We present a simple randomized protocol that works in O(n(2)/log n) completion time. This bound is then shown to be optimal.
This paper studies broadcasting and gossiping algorithms in random and general AdHoc networks. Our goal is not only to minimise the broadcasting and gossiping time, but also to minimise the energy consumption, which i...
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ISBN:
(纸本)9781595936677
This paper studies broadcasting and gossiping algorithms in random and general AdHoc networks. Our goal is not only to minimise the broadcasting and gossiping time, but also to minimise the energy consumption, which is measured in terms of the total number of messages (or transmissions) sent,. We assume that the nodes of the network do not know the network, and that they cart only send with a fixed power, meaning they can not adjust the area sizes that their messages cover. We believe that under these circumstances the number of transmissions is a very good measure for the overall energy consumption. For random networks;we present a broadcasting algorithm where every node transmits at most once. We show that our algorithm broadcasts in O(log n) steps, w.h.p., where n is the number of nodes. We then present a O(d log n) (d is the expected degree) gossiping algorithm using O(log n) messages per node. For general networks with known diameter D, we present a randomised broadcasting algorithm with optimal broadcasting time O(D log(n/D) + log(2)n) that uses art expected number of O(log(2) n/ log(n/D)) transmissions per node. We also show a tradeoff result between the broadcasting time and the number of transmissions: we construct a network such that any oblivious algorithm using a tinge-invariant distribution requires Q(log2 n/ log(n/D)) messages per node in order to finish broadcasting in optimal time. This demonstrates the tightness of our upper bound. We also show that no oblivious algorithm can complete broadcasting w.h.p. using o(log n) messages per node.
Consider the following Steiner Tree leasing problem. Given a graph G = (V, E) with root r, and a sequence of terminal sets D-t subset of V for each day t is an element of [T]. A feasible solution to the problem is a s...
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ISBN:
(纸本)9783540727910
Consider the following Steiner Tree leasing problem. Given a graph G = (V, E) with root r, and a sequence of terminal sets D-t subset of V for each day t is an element of [T]. A feasible solution to the problem is a set of edges E-t for each t connecting D-t to r. Instead of obtaining edges for a single day at a time, or for infinitely long (both of which give Steiner tree problems), we lease edges for say, { a day, a week, a month, a year}. Naturally, leasing an edge for a longer period costs less per unit of time. What is a good leasing strategy? In this paper, we give a general approach to solving a wide class of such problems by showing a close connection between deterministic leasing problems and problems in multistage stochastic optimization. All our results are in the offline setting.
ne scenario optimization method developed in [1] is a theoretically sound and practically effective technique for solving in a probabilistic setting robust convex optimization problems arising in systems and control d...
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ISBN:
(纸本)9781424414970
ne scenario optimization method developed in [1] is a theoretically sound and practically effective technique for solving in a probabilistic setting robust convex optimization problems arising in systems and control design, that would otherwise be hard to tackle via standard deterministic techniques. In this note, we further explore some aspects of the scenario methodology, and present two results pertaining to the tightness of the sample complexity bounds. We also state a new theorem that enables the user to make a-priori probabilistic claims on the scenario solution, with one level of probability only.
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