12,680 research outputs found

    A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem

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    The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms are able to find solutions as good as approximation algorithms do. This paper presents a novel multi-objective optimisation genetic algorithm for solving the 0-1 knapsack problem. Experiment results show that the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm

    Fast and scalable inference of multi-sample cancer lineages.

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    Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee

    An efficient null space inexact Newton method for hydraulic simulation of water distribution networks

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    Null space Newton algorithms are efficient in solving the nonlinear equations arising in hydraulic analysis of water distribution networks. In this article, we propose and evaluate an inexact Newton method that relies on partial updates of the network pipes' frictional headloss computations to solve the linear systems more efficiently and with numerical reliability. The update set parameters are studied to propose appropriate values. Different null space basis generation schemes are analysed to choose methods for sparse and well-conditioned null space bases resulting in a smaller update set. The Newton steps are computed in the null space by solving sparse, symmetric positive definite systems with sparse Cholesky factorizations. By using the constant structure of the null space system matrices, a single symbolic factorization in the Cholesky decomposition is used multiple times, reducing the computational cost of linear solves. The algorithms and analyses are validated using medium to large-scale water network models.Comment: 15 pages, 9 figures, Preprint extension of Abraham and Stoianov, 2015 (https://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001089), September 2015. Includes extended exposition, additional case studies and new simulations and analysi

    Optimal Placement of Phasor Measurement Units for Power Systems Using Genetic Algorithm

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    Power grids require monitoring to operate with high efficiency while minimizing the chances of having a failure. However, current monitoring scheme which consists of SCADA (Supervisory Control and Data Acquisition), accompanied with conventional meters distributed throughout the grid, is no longer sufficient to maintain an acceptable operation of the grid. This is evident from the multiple failures and blackouts that happened and are still happening in grids worldwide. This issue became more severe due to systems being operated near their limits (to reduce costs and due to the increase in electricity demands), as well as, the addition of renewable energy sources, which usually have abrupt changes. Smart grids were introduced as a solution to this issue by the inclusion of Wide Area Monitoring System (WAMS), which is mainly based on Phasor Measurement Units (PMU), which are measurement devices that provides synchronized time stamped measurements with high sending rate which significantly improves the monitoring of the grid. However, PMUs are relatively expensive (considering both direct and indirect costs incurred). Thus, it is desired to know the minimum number of PMUs required for achieving certain monitoring criteria. Thus, Optimal PMU Placement (OPP) formulates an optimization problem to solve this issue. In the literature of OPP, multiple objectives and constraints are considered, based on desired criteria. In this thesis, a review of OPP is made, followed by the application of selected algorithms (Integer Linear Programming and Genetic Algorithm) on various test systems as a verification and then applying it to Qatar Grid, to compare between different considerations as well as gain insight about the possible PMU placements for Qatar Grid. The contribution of this thesis is introducing a modified fitness function for the Genetic Algorithm that provides more diverse results than previous papers, while incorporating for various considerations like Zero Injection Buses, Conventional Measurements and current branch limit. It also analyzes the results of current branch limit and provides new plots describing their effects

    De novo construction of polyploid linkage maps using discrete graphical models

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    Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the density of genetic markers. Such revolutionary advances in technology have given rise to new challenges, such as creating high-density linkage maps. Current multiple testing approaches based on pairwise recombination fractions are underpowered in the high-dimensional setting and do not extend easily to polyploid species. We propose to construct linkage maps using graphical models either via a sparse Gaussian copula or a nonparanormal skeptic approach. Linkage groups (LGs), typically chromosomes, and the order of markers in each LG are determined by inferring the conditional independence relationships among large numbers of markers in the genome. Through simulations, we illustrate the utility of our map construction method and compare its performance with other available methods, both when the data are clean and contain no missing observations and when data contain genotyping errors and are incomplete. We apply the proposed method to two genotype datasets: barley and potato from diploid and polypoid populations, respectively. Our comprehensive map construction method makes full use of the dosage SNP data to reconstruct linkage map for any bi-parental diploid and polyploid species. We have implemented the method in the R package netgwas.Comment: 25 pages, 7 figure

    Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

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    Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instance
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