7,688 research outputs found
Design-Time Quantification of Integrity in Cyber-Physical-Systems
In a software system it is possible to quantify the amount of information
that is leaked or corrupted by analysing the flows of information present in
the source code. In a cyber-physical system, information flows are not only
present at the digital level, but also at a physical level, and to and fro the
two levels. In this work, we provide a methodology to formally analyse a
Cyber-Physical System composite model (combining physics and control) using an
information flow-theoretic approach. We use this approach to quantify the level
of vulnerability of a system with respect to attackers with different
capabilities. We illustrate our approach by means of a water distribution case
study
Efficient Cluster Algorithm for Spin Glasses in Any Space Dimension
Spin systems with frustration and disorder are notoriously difficult to study
both analytically and numerically. While the simulation of ferromagnetic
statistical mechanical models benefits greatly from cluster algorithms, these
accelerated dynamics methods remain elusive for generic spin-glass-like
systems. Here we present a cluster algorithm for Ising spin glasses that works
in any space dimension and speeds up thermalization by at least one order of
magnitude at temperatures where thermalization is typically difficult. Our
isoenergetic cluster moves are based on the Houdayer cluster algorithm for
two-dimensional spin glasses and lead to a speedup over conventional
state-of-the-art methods that increases with the system size. We illustrate the
benefits of the isoenergetic cluster moves in two and three space dimensions,
as well as the nonplanar chimera topology found in the D-Wave Inc.~quantum
annealing machine.Comment: 5 pages, 4 figure
The Local Optima Level in Chemotherapy Schedule Optimisation
In this paper a multi-drug Chemotherapy Schedule Optimisation Problem (CSOP) is subject to Local Optima Network (LON) analysis. LONs capture global patterns in fitness landscapes. CSOPs have not previously been subject to fitness landscape analysis. We fill this gap: LONs are constructed and studied for meaningful structure. The CSOP formulation presents novel challenges and questions for the LON model because there are infeasible regions in the fitness landscape and an unknown global optimum; it also brings a topic from healthcare to LON analysis. Two LON Construction algorithms are proposed for sampling CSOP fitness landscapes: a Markov-Chain Construction Algorithm and a Hybrid Construction Algorithm. The results provide new insight into LONs of highly-constrained spaces, and into the proficiency of search operators on the CSOP. Iterated Local Search and Memetic Search, which are the foundations for the LON algorithms, are found to markedly out-perform a Genetic Algorithm from the literature
A software interface for supporting the application of data science to optimisation
Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as
`HyFlex' to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control
Best-case performance of quantum annealers on native spin-glass benchmarks: How chaos can affect success probabilities
Recent tests performed on the D-Wave Two quantum annealer have revealed no
clear evidence of speedup over conventional silicon-based technologies. Here,
we present results from classical parallel-tempering Monte Carlo simulations
combined with isoenergetic cluster moves of the archetypal benchmark problem-an
Ising spin glass-on the native chip topology. Using realistic uncorrelated
noise models for the D-Wave Two quantum annealer, we study the best-case
resilience, i.e., the probability that the ground-state configuration is not
affected by random fields and random-bond fluctuations found on the chip. We
thus compute classical upper-bound success probabilities for different types of
disorder used in the benchmarks and predict that an increase in the number of
qubits will require either error correction schemes or a drastic reduction of
the intrinsic noise found in these devices. We outline strategies to develop
robust, as well as hard benchmarks for quantum annealing devices, as well as
any other computing paradigm affected by noise.Comment: 8 pages, 5 figure
Memory distribution in complex fitness landscapes
In a co-evolutionary context, the survive probability of individual elements
of a system depends on their relation with their neighbors. The natural
selection process depends on the whole population, which is determined by local
events between individuals. Particular characteristics assigned to each
individual, as larger memory, usually improve the individual fitness, but an
agent possess also endogenous characteristics that induce to re-evaluate her
fitness landscape and choose the best-suited kind of interaction, inducing a
non absolute value of the outcomes of the interaction. In this work, a novel
model with agents combining memory and rational choice is introduced, where
individual choices in a complex fitness landscape induce changes in the
distribution of the number of agents as a function of the time. In particular,
the tail of this distribution is fat compared with distributions for agents
interacting only with memory.Comment: 6 pages, 3 figures, submited to Physica
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