Computer network reliability optimization under double-resource assignments subject to a transmission budget

TL;DR: The correctness of the proposed Depth-First-Search (DFS)-based algorithm is proven, and an example is provided to illustrate the generation of all (d, t)-QPs, and the analysis of the algorithm's computational complexity and computer experiments indicate that it is more efficient than known algorithms.

TL;DR: A straightforward and simple algorithm based on a novel concept called the deterioration-effect d -minimal path ( d -MP de) is presented to evaluate the reliability of an MFN de, which is defined as the probability that at least d units of data can be received by the sink node through the MFNDe.

TL;DR: A bacterial-inspired evolutionary algorithm (BiEA) for addressing redundancy allocation problems (RAPs) and case studies on MLRAP designs show the BiEA outperforms current state-of-the-art approaches in regard to two representative experiments.

TL;DR: This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network.

TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.

TL;DR: The techniques presented by Ford and Fulkerson spurred the development of powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming.

TL;DR: In this article, the authors introduce genetic algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers.