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GICS follows the outline of Algorithm 20, Section 65 Naturally, the chromosome encodes the processor allocation, which is an indirect representation (Section 652) Hence, the construction of the schedule corresponding to each chromosome, which is necessary for the evaluation of its tness, requires the application of a heuristic As already mentioned, this is performed by a list scheduling heuristic under the involvement contention model (see later discussion) Strictly speaking, this means that a two-phase heuristic is applied multiple times in GICS once for each new chromosome To reduce the running time of the evaluation, the node order is determined only once at the beginning of the algorithm, namely, according to their bottom levels Like most GAs, GICS starts with a random initial population, enhanced with a chromosome representing a sequential schedule (all nodes are allocated to a single processor) The pool is completed by one allocation extracted from a schedule produced with a list scheduling heuristic (Section 841) Most other components are fairly standard In fact, the algorithm can even be employed for scheduling under the classic or the contention model, using a modi ed evaluation (ie, scheduling heuristic) Phase 2 List Scheduling From Section 52 it is known that the second phase can be performed with a simple list scheduling algorithm The processor choice of list scheduling is simply a lookup from the given mapping A established in the rst phase Even though list scheduling under the involvement contention model was already studied in Section 841, it must be revisited as there are three alternatives for the scheduling of the edges when the processor allocation is already given (Section 832) Scheduling of the Edges The rst alternative corresponds to direct scheduling, and the procedures for the scheduling of a node and the calculation of the DRT are presented in Algorithms 23 and 24 The scheduling of an edge on the source processor must be modi ed (line 16 in Algorithm 23), since it is no longer necessary to assume the worst case Lines 1 5 (removing local edges) and line 9 (correcting the nish time) can be dropped completely, since the provisional scheduling of the edges is not necessary with the given processor allocation The other two alternatives are only distinguished from this procedure through the place where the edges are scheduled on the links and the destination processor So, for the second alternative, where the edge is scheduled on the links as a leaving edge, lines 8 and 10 of Algorithm 23 move to the for loop on lines 15 17 In the third alternative, the edge is also scheduled on the destination processor within this loop, and the for loop for the entering edges (lines 6 13) is completely dropped Of course, in the determination of the DRT the entering edges do not need to be tentatively scheduled, as the processor allocation is already given Complexity The complexity of list scheduling with a given processor allocation decreases slightly in comparison to list scheduling with start/ nish time minimization It is not necessary to tentatively schedule a node and its edges on every processor Thus,.

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at each stage, and reliability, cost and weight are objective functions In these papers different methodologies are explored: in [57], to help the decision maker make a selection, a pseudo-ranking scheme and clustering techniques to reduce the size of the Pareto optimal set are presented; in [58] a multiple objective genetic algorithm for solving the problem is given Marseguerra et al [47] used a stochastic model for network reliability, considering a function of imperfectly known reliability parameters of network components The problem to solve is again a multiobjective one, the aim being to nd the network topologies that maximize the network reliability and minimize the variance of this estimation (taking into account the imperfectly known reliability parameters) The decision variable is the type and the redundancy level of components to be allocated within a xed network topology, where each component has an associated reliability probability distribution The optimization method is based on genetic algorithms, and a Monte Carlo evaluation algorithm is used o incorporate the uncertainty in the reliability values; the repeated evaluations of the good individuals are accumulated, to enhance the signi cance of the estimations The numerical examples consider only very small networks (with 7 and 8 links), and allow the Pareto optimal solutions obtained to be examined and the differences in the con gurations to be easily identi ed Premprayoon and Wardkein [50] tackle another variant of the reliable network design problem, where it is possible to de ne for each pair of nodes whether they will be connected by a link, whose characteristics (cost and reliability) can also be chosen from a given set The objective is to minimize network cost subject to a requirement of attaining at least a given reliability level The authors compare an ant colony optimization method, a tabu search method and a local search method; the network reliability evaluation is done by backtracking (as only very small network topologies are studied) The best computational results are obtained by the ant colony optimization Cook and Ramirez-Marquez [19] study mobile ad hoc wireless networks, in particular in a military context These networks have their own characteristics, which this work describes, and a proposal is presented on how to adapt the classical analysis of network reliability to this new context The methods proposed rely on considering the effect of node mobility and the continuous changes in the network s connectivity Wakabayashi [61] also studies highway network reliability, taking normal and abnormal periods into account The motivation of this work is to detect the critical link in the network, whose improvement will lead to reliability improvement for the larger network This paper presents a comparative study between using a probability importance index (Birnbaum s structural importance) and a criticality importance based on network reliability measures, which address some problems in Birnbaum s index Li [45] proposes to employ an accessible node rate index based on two-terminal reliability to evaluate the anti-disaster level of a city road network In particular, this author evaluates the connectedness to the start points of emergency vehicles, and provides a method of accessing such accessibility.

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3 Please make sure to reference the correct version . More information about the Barcode class can be found in the . The class is contained in the namespace TECIT .Related: 

Provide detailed .NET barcode guide, Word barcode guide & C# barcode tutorial guides for . Mature and reliable Code 128 encoder control addin for .NET used world ide.Related: QR Code Generator Word Image, QR Code Generator Excel Image, .NET QR Code Generation Data

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