1,998 research outputs found
Various heuristic algorithms to minimise the two-page crossing numbers of graphs
We propose several new heuristics for the twopage book crossing problem, which are based on recent algorithms for the corresponding one-page problem. Especially, the neural network model for edge allocation is combined for the first time with various one-page algorithms. We investigate the performance of the new heuristics by testing them on various benchmark test suites. It is found out that the new heuristics outperform the previously known heuristics and produce good approximations of the planar crossing number for severalwell-known graph families. We conjecture that the optimal two-page drawing of a graph represents the planar drawing of the graph
Experimental Evaluation of Book Drawing Algorithms
A -page book drawing of a graph consists of a linear ordering of
its vertices along a spine and an assignment of each edge to one of the
pages, which are half-planes bounded by the spine. In a book drawing, two edges
cross if and only if they are assigned to the same page and their vertices
alternate along the spine. Crossing minimization in a -page book drawing is
NP-hard, yet book drawings have multiple applications in visualization and
beyond. Therefore several heuristic book drawing algorithms exist, but there is
no broader comparative study on their relative performance. In this paper, we
propose a comprehensive benchmark set of challenging graph classes for book
drawing algorithms and provide an extensive experimental study of the
performance of existing book drawing algorithms.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Genetic algorithms for the 2-page book drawing problem of graphs
The minimisation of edge crossings in a book drawing of a graph
is one of the important goals for a linear VLSI design, and the 2-page
crossing number of a graph provides an upper bound for the standard
planar crossing number. We design genetic algorithms for the 2-page
drawings, and test them on the benchmark test suits, Rome graphs and
Random Connected Graphs. We also test some circulant graphs, and
get better results than previously presented in the literature. Moreover,
we formalise three conjectures for certain kinds of circulant graphs,
supported by our experimental results
Heuristic crossing minimisation algorithms for the two-page drawing problem
The minimisation of edge crossings in a book drawing of a graph G is one of the
important goals for a linear VLSI design, and the two-page crossing number of a graph G provides
an upper bound for the standard planar crossing number. We propose several new heuristics for
the two-page drawing problem, and test them on benchmark test suites, Rome graphs and Random
Connected Graphs. We also test some typical graphs, and get some exact results. The results for
some circulant graphs are better than the one presented by Cimikowski. We have a conjecture for
cartesian graphs, supported by our experimental results, and provide direct methods to get optimal
solutions for 3- or 4-row meshes and Halin graphs
Parallelisation of genetic algorithms for the 2-page crossing number problem
Genetic algorithms have been applied to solve the 2-page crossing number problem
successfully, but since they work with one global population, the search time and
space are limited. Parallelisation provides an attractive prospect to improve the efficiency
and solution quality of genetic algorithms. This paper investigates the complexity
of parallel genetic algorithms (PGAs) based on two evaluation measures:
Computation-time to Communication-time and Population-size to Chromosomesize.
Moreover, the paper unifies the framework of PGA models with the function
PGA (subpopulation size; cluster size, migration period; topology), and explores
the performance of PGAs for the 2-page crossing number problem
Reinforcement Learning for Racecar Control
This thesis investigates the use of reinforcement learning to learn to drive a racecar in the simulated environment of the Robot Automobile Racing Simulator. Real-life race driving is known to be difficult for humans, and expert human drivers use complex sequences of actions. There are a large number of variables, some of which change stochastically and all of which may affect the outcome. This makes driving a promising domain for testing and developing Machine Learning techniques that have the potential to be robust enough to work in the real world. Therefore the principles of the algorithms from this work may be applicable to a range of problems.
The investigation starts by finding a suitable data structure to represent the information learnt. This is tested using supervised learning. Reinforcement learning is added and roughly tuned, and the supervised learning is then removed. A simple tabular representation is found satisfactory, and this avoids difficulties with more complex methods and allows the investigation to concentrate on the essentials of learning. Various reward sources are tested and a combination of three are found to produce the best performance. Exploration of the problem space is investigated. Results show exploration is essential but controlling how much is done is also important. It turns out the learning episodes need to be very long and because of this the task needs to be treated as continuous by using discounting to limit the size of the variables stored. Eligibility traces are used with success to make the learning more efficient. The tabular representation is made more compact by hashing and more accurate by using smaller buckets. This slows the learning but produces better driving. The improvement given by a rough form of generalisation indicates the replacement of the tabular method by a function approximator is warranted. These results show reinforcement learning can work within the Robot Automobile Racing Simulator, and lay the foundations for building a more efficient and competitive agent
Various island-based parallel genetic algorithms for the 2-page drawing problem
Genetic algorithms have been applied to solve the
2-page drawing problem successfully, but they work
with one global population, so the search time and
space are limited. Parallelization provides an attractive
prospect in improving the efficiency and solution
quality of genetic algorithms. One of the most popular
tools for parallel computing is Message Passing
Interface (MPI). In this paper, we present four island
models of Parallel Genetic Algorithms with MPI: island
models with linear, grid, random graph topologies,
and island model with periodical synchronisation.
We compare their efficiency and quality of solutions for
the 2-page drawing problem on a variety of graphs
Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations
制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568
Loom: Query-aware Partitioning of Online Graphs
As with general graph processing systems, partitioning data over a cluster of
machines improves the scalability of graph database management systems.
However, these systems will incur additional network cost during the execution
of a query workload, due to inter-partition traversals. Workload-agnostic
partitioning algorithms typically minimise the likelihood of any edge crossing
partition boundaries. However, these partitioners are sub-optimal with respect
to many workloads, especially queries, which may require more frequent
traversal of specific subsets of inter-partition edges. Furthermore, they
largely unsuited to operating incrementally on dynamic, growing graphs.
We present a new graph partitioning algorithm, Loom, that operates on a
stream of graph updates and continuously allocates the new vertices and edges
to partitions, taking into account a query workload of graph pattern
expressions along with their relative frequencies.
First we capture the most common patterns of edge traversals which occur when
executing queries. We then compare sub-graphs, which present themselves
incrementally in the graph update stream, against these common patterns.
Finally we attempt to allocate each match to single partitions, reducing the
number of inter-partition edges within frequently traversed sub-graphs and
improving average query performance.
Loom is extensively evaluated over several large test graphs with realistic
query workloads and various orderings of the graph updates. We demonstrate
that, given a workload, our prototype produces partitionings of significantly
better quality than existing streaming graph partitioning algorithms Fennel and
LDG
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