12,166 research outputs found
Population extremal optimisation for discrete multi-objective optimisation problems
The power to solve intractable optimisation problems is often found through population based evolutionary methods. These include, but are not limited to, genetic algorithms, particle swarm optimisation, differential evolution and ant colony optimisation. While showing much promise as an effective optimiser, extremal optimisation uses only a single solution in its canonical form ā and there are no standard population mechanics. In this paper, two population models for extremal optimisation are proposed and applied to a multi-objective version of the generalised assignment problem. These models use novel intervention/interaction strategies as well as collective memory in order to allow individual population members to work together. Additionally, a general non-dominated local search algorithm is developed and tested. Overall, the results show that improved attainment surfaces can be produced using population based interactions over not using them. The new EO approach is also shown to be highly competitive with an implementation of NSGA-II.No Full Tex
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological
parameters, to functional biomarkers, and represent an invaluable tool for
longitudinal and cross-sectional clinical inference. This, however, is only
feasible when graphs are co-registered together, allowing coherent multiple
comparisons. The robust registration of vascular topologies stands therefore as
key enabling technology for group-wise analyses. In this work, we present an
end-to-end vascular graph registration approach, that aligns networks with
non-linear geometries and topological deformations, by introducing a novel
overconnected geodesic vascular graph formulation, and without enforcing any
anatomical prior constraint. The 3D elastic graph registration is then
performed with state-of-the-art graph matching methods used in computer vision.
Promising results of vascular matching are found using graphs from synthetic
and real angiographies. Observations and future designs are discussed towards
potential clinical applications
The Day-to-Day Dynamics of Route Choice
This paper reviews methods proposed for modelling the day-to-day dynamics of route choice, on an individual driver level. Extensions to within-day dynamics and choice of departure time are also discussed. A new variation on the approaches reviewed is also described. Simulation tests on a simple two-link network are used to illustrate the approach, and to investigate probabilistic counterparts of equilibrium uniqueness and stability. The long-term plan is for such a day-to-day varying demand-side model to be combined with a suitable microscopic supply-side model, thereby producing a new generation network model. The need for such a model - particularly in the context of assessing real-time transport strategies - has been identified in previous working papers
Discrete-time rewards model-checked
This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures ā involving expected as well as accumulated rewards ā in a precise and succinct way. Algorithms to efficiently analyze such formulae are introduced. The approach is illustrated by model-checking a probabilistic cost model of the IPv4 zeroconf protocol for distributed address assignment in ad-hoc networks
Program Synthesis and Linear Operator Semantics
For deterministic and probabilistic programs we investigate the problem of
program synthesis and program optimisation (with respect to non-functional
properties) in the general setting of global optimisation. This approach is
based on the representation of the semantics of programs and program fragments
in terms of linear operators, i.e. as matrices. We exploit in particular the
fact that we can automatically generate the representation of the semantics of
elementary blocks. These can then can be used in order to compositionally
assemble the semantics of a whole program, i.e. the generator of the
corresponding Discrete Time Markov Chain (DTMC). We also utilise a generalised
version of Abstract Interpretation suitable for this linear algebraic or
functional analytical framework in order to formulate semantical constraints
(invariants) and optimisation objectives (for example performance
requirements).Comment: In Proceedings SYNT 2014, arXiv:1407.493
Probabilistic data flow analysis: a linear equational approach
Speculative optimisation relies on the estimation of the probabilities that
certain properties of the control flow are fulfilled. Concrete or estimated
branch probabilities can be used for searching and constructing advantageous
speculative and bookkeeping transformations.
We present a probabilistic extension of the classical equational approach to
data-flow analysis that can be used to this purpose. More precisely, we show
how the probabilistic information introduced in a control flow graph by branch
prediction can be used to extract a system of linear equations from a program
and present a method for calculating correct (numerical) solutions.Comment: In Proceedings GandALF 2013, arXiv:1307.416
- ā¦