13,260 research outputs found
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
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Renewable and sustainable energy is one of the most important challenges
currently facing mankind. Wind has made an increasing contribution to the
world's energy supply mix, but still remains a long way from reaching its full
potential. In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically instantiated
and evaluated under approximated wind tunnel conditions. An artificial neural
network is used as a surrogate model to assist learning and found to reduce the
number of fabrications required to reach a higher aerodynamic efficiency,
resulting in an important cost reduction. Unlike in other approaches, such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made.Comment: 14 pages, 11 figure
Interactive constraint-based space layout planning
Layout planning is the primordial design activity that determines the characteristics and
performance of a building throughout its lifecycle. Due to its iterative nature, there is a growing
interest in the automation of space layout planning to enhance the search for optimum design
solutions. The approaches for automation range from constraint/heuristics-based to the
application of numerical optimisation algorithms. Among these, the use of design constraints to
guide the search of the solution space is well regarded due to its ability to model design
problems of an applied nature with multiple objectives. Constraint-based approaches also allow
interactivity between the designer and layout planning process, which simulates the iterative
nature of creative design and can be integrated well with the existing design process.
Interactivity also enhances the management of design knowledge through improved processing
and visualisation of information. This paper presents a theoretical framework for interactive
constraint-based layout optimisation with an implemented prototype for a hospital patient room
interior layout.
The theoretical framework was developed by analysing existing layout automation methods and
interactive approaches through a review of relevant literature. Object-oriented computer
programming was used to develop the prototype to demonstrate the proposed approach of
interactive layout planning system. The framework augments the iterative design process by
facilitating the active participation and sharing of the designer’s knowledge during the
aggregation. With regard to the implementation of the framework in large problems, fast
evaluation of design solution was found to be necessary to interact with the system in real time.
Interactive constraint-based layout optimisation has, therefore, the ability to enhance the search
process of optimum design solutions by augmenting the iterative nature of the creative design
process
Genetic algorithms
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
The Optimum Combination Of Local Searches For Genetic Operators In Memetic Algorithm For The Space Allocation Problem [QA9.58. S624 2008 f rb].
Dalam tesis ini, kami membuat penyelidikan mengenai pengagihan ruang di universiti. Kajian ini memfokus kepada pengagihan ruang dalam penyediaan jadual waktu.
This thesis investigates the university space allocation problem, which focuses on the distribution of events among the available venues, without violating any hard constraints
while satisfying as many soft constraints as possible and ensure optimum space utilization
TensorFlow Enabled Genetic Programming
Genetic Programming, a kind of evolutionary computation and machine learning
algorithm, is shown to benefit significantly from the application of vectorized
data and the TensorFlow numerical computation library on both CPU and GPU
architectures. The open source, Python Karoo GP is employed for a series of 190
tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data
points. This body of tests demonstrates that datasets measured in tens and
hundreds of data points see 2-15x improvement when moving from the scalar/SymPy
configuration to the vector/TensorFlow configuration, with a single core
performing on par or better than multiple CPU cores and GPUs. A dataset
composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core
performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing
5.5M data points sees GPU configurations out-performing CPU configurations on
average by 1.3x.Comment: 8 pages, 5 figures; presented at GECCO 2017, Berlin, German
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