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Solving the minimum labelling spanning tree problem using hybrid local search
Given a connected, undirected graph whose edges are labelled (or coloured), the minimum
labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest
number of distinct labels (or colours). In recent work, the MLST problem has been shown
to be NP-hard and some effective heuristics (Modified Genetic Algorithm (MGA) and Pilot
Method (PILOT)) have been proposed and analyzed. A hybrid local search method, that we
call Group-Swap Variable Neighbourhood Search (GS-VNS), is proposed in this paper. It is
obtained by combining two classic metaheuristics: Variable Neighbourhood Search (VNS) and
Simulated Annealing (SA). Computational experiments show that GS-VNS outperforms MGA
and PILOT. Furthermore, a comparison with the results provided by an exact approach shows
that we may quickly obtain optimal or near-optimal solutions with the proposed heuristic
Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in
the broad field of computational intelligence. Accordingly, a wide repertoire
of pattern recognition tools, such as classifiers and knowledge discovery
procedures, are nowadays available and tested for various datasets of labeled
graphs. However, the design of effective learning procedures operating in the
space of labeled graphs is still a challenging problem, especially from the
computational complexity viewpoint. In this paper, we present a major
improvement of a general-purpose classifier for graphs, which is conceived on
an interplay between dissimilarity representation, clustering,
information-theoretic techniques, and evolutionary optimization algorithms. The
improvement focuses on a specific key subroutine devised to compress the input
data. We prove different theorems which are fundamental to the setting of the
parameters controlling such a compression operation. We demonstrate the
effectiveness of the resulting classifier by benchmarking the developed
variants on well-known datasets of labeled graphs, considering as distinct
performance indicators the classification accuracy, computing time, and
parsimony in terms of structural complexity of the synthesized classification
models. The results show state-of-the-art standards in terms of test set
accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio
Fast and scalable inference of multi-sample cancer lineages.
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
Compressing DNA sequence databases with coil
Background: Publicly available DNA sequence databases such as GenBank are large, and are
growing at an exponential rate. The sheer volume of data being dealt with presents serious storage
and data communications problems. Currently, sequence data is usually kept in large "flat files,"
which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which
rarely achieves good compression ratios. While much research has been done on compressing
individual DNA sequences, surprisingly little has focused on the compression of entire databases
of such sequences. In this study we introduce the sequence database compression software coil.
Results: We have designed and implemented a portable software package, coil, for compressing
and decompressing DNA sequence databases based on the idea of edit-tree coding. coil is geared
towards achieving high compression ratios at the expense of execution time and memory usage
during compression – the compression time represents a "one-off investment" whose cost is
quickly amortised if the resulting compressed file is transmitted many times. Decompression
requires little memory and is extremely fast. We demonstrate a 5% improvement in compression
ratio over state-of-the-art general-purpose compression tools for a large GenBank database file
containing Expressed Sequence Tag (EST) data. Finally, coil can efficiently encode incremental
additions to a sequence database.
Conclusion: coil presents a compelling alternative to conventional compression of flat files for the
storage and distribution of DNA sequence databases having a narrow distribution of sequence
lengths, such as EST data. Increasing compression levels for databases having a wide distribution of
sequence lengths is a direction for future work
Optimisation of piping network design for district cooling system
A district cooling system (DeS) is a.scheme for centralised cooling energy distribution which takes advantage of economies of scale and load diversity. . A cooling medium (chilled water) is generated at a central refrigeration plant and then supplied to a district area, comprising multiple buildings, through a closed-loop piping circuit. Because of the substantial capital investment involved, an optimal design of the distribution piping . configuration is one of the crucial factors for successful implementation of a district 1'. cooling scheme. Since there. exists an enormous number of different combinations of the piping configuration, it is not feasible to evaluate each individual case using an exhaustive approach. This thesis exammes the problem of determining an optimal distribution piping configuration using a genetic algorithm (GA). In order to estimate the spatial and temporal distribution of cooling loads; the climatic conditions of Hong Kong were investigated and a weather database in the form of a typical meteorological year (TMY) was developed. Detailed thermal modelling of a number of prototypical buildings was carried out to determine benchmark cooling loads. A novel Local Search/Looped Local Search algorithm was developed for finding optimal/near-optimal distribution piping configurations. By means of computational . experiments, it was demonstrated that there is a promising improvement to GA performance by including the Local Search/Looped Local Search algorithm, in terms of both solution quality and computational efficiency. The effects on the search performance of a number of parameters were systematically investigated to establish the most effective settings. In order to illustrate the effectiveness of the Local Search/Looped Local Search algorithm, a benchmark problem - the optimal communication,spanning tree (OCST) was used for comparison. The results showed that the Looped Local Search method developed in this work was an effective tool for optimal network design of the distribution piping system in DCS, as well as for optimising the OCST problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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