2,294 research outputs found
On combinatorial optimisation in analysis of protein-protein interaction and protein folding networks
Abstract: Protein-protein interaction networks and protein folding networks represent prominent research topics at the intersection of bioinformatics and network science. In this paper, we present a study of these networks from combinatorial optimisation point of view. Using a combination of classical heuristics and stochastic optimisation techniques, we were able to identify several interesting combinatorial properties of biological networks of the COSIN project. We obtained optimal or near-optimal solutions to maximum clique and chromatic number problems for these networks. We also explore patterns of both non-overlapping and overlapping cliques in these networks. Optimal or near-optimal solutions to partitioning of these networks into non-overlapping cliques and to maximum independent set problem were discovered. Maximal cliques are explored by enumerative techniques. Domination in these networks is briefly studied, too. Applications and extensions of our findings are discussed
Framework for sustainable TVET-Teacher Education Program in Malaysia Public Universities
Studies had stated that less attention was given to the education aspect, such as
teaching and learning in planning for improving the TVET system. Due to the 21st
Century context, the current paradigm of teaching for the TVET educators also has
been reported to be fatal and need to be shifted. All these disadvantages reported
hindering the country from achieving the 5th strategy in the Strategic Plan for
Vocational Education Transformation to transform TVET system as a whole.
Therefore, this study aims to develop a framework for sustainable TVET Teacher
Education program in Malaysia. This study had adopted an Exploratory Sequential
Mix-Method design, which involves a semi-structured interview (phase one) and
survey method (phase two). Nine experts had involved in phase one chosen by using
Purposive Sampling Technique. As in phase two, 118 TVET-TE program lecturers
were selected as the survey sample chosen through random sampling method. After
data analysis in phase one (thematic analysis) and phase two (Principal Component
Analysis), eight domains and 22 elements have been identified for the framework for
sustainable TVET-TE program in Malaysia. This framework was identified to embed
the elements of 21st Century Education, thus filling the gap in this research. The
research findings also indicate that the developed framework was unidimensional and
valid for the development and research regarding TVET-TE program in Malaysia.
Lastly, it is in the hope that this research can be a guide for the nations in producing a
quality TVET teacher in the future
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
A significant challenge in nature-inspired algorithmics is the identification
of specific characteristics of problems that make them harder (or easier) to
solve using specific methods. The hope is that, by identifying these
characteristics, we may more easily predict which algorithms are best-suited to
problems sharing certain features. Here, we approach this problem using fitness
landscape analysis. Techniques already exist for measuring the "difficulty" of
specific landscapes, but these are often designed solely with evolutionary
algorithms in mind, and are generally specific to discrete optimisation. In
this paper we develop an approach for comparing a wide range of continuous
optimisation algorithms. Using a fitness landscape generation technique, we
compare six different nature-inspired algorithms and identify which methods
perform best on landscapes exhibiting specific features.Comment: 10 pages, 1 figure, submitted to the 11th International Conference on
Adaptive and Natural Computing Algorithm
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