757 research outputs found
Solving and Verifying the Boolean Pythagorean Triples Problem via Cube-and-Conquer
We solved a long-outstanding open problem in Ramsey theory, using SAT solving
Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis
The hyperclimbing hypothesis is a hypothetical explanation for adaptation in
genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an
intuitive, general-purpose, non-local search heuristic applicable to discrete
product spaces with rugged or stochastic cost functions. The strength of this
heuristic lie in its insusceptibility to local optima when the cost function is
deterministic, and its tolerance for noise when the cost function is
stochastic. Hyperclimbing works by decimating a search space, i.e. by
iteratively fixing the values of small numbers of variables. The hyperclimbing
hypothesis holds that UGAs work by implementing efficient hyperclimbing. Proof
of concept for this hypothesis comes from the use of a novel analytic technique
involving the exploitation of algorithmic symmetry. We have also obtained
experimental results that show that a simple tweak inspired by the
hyperclimbing hypothesis dramatically improves the performance of a UGA on
large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin
Glasses problem.Comment: 22 pages, 5 figure
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems
Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high level heuristics during the problem solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism which contains a population of both high quality and diverse solutions that is updated during the problem solving process. The generality of the proposed hyper-heuristic is validated against six well known combinatorial optimization problem, with very different landscapes, provided by the HyFlex software. Empirical results comparing the proposed hyper-heuristic with state of the art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains
"So, Tell Me What Users Want, What They Really, Really Want!"
Equating users' true needs and desires with behavioural measures of
'engagement' is problematic. However, good metrics of 'true preferences' are
difficult to define, as cognitive biases make people's preferences change with
context and exhibit inconsistencies over time. Yet, HCI research often glosses
over the philosophical and theoretical depth of what it means to infer what
users really want. In this paper, we present an alternative yet very real
discussion of this issue, via a fictive dialogue between senior executives in a
tech company aimed at helping people live the life they `really' want to live.
How will the designers settle on a metric for their product to optimise
Cophylogenetic analysis of dated trees
Parasites and the associations they form with their hosts is an important area of research due to the associated health risks which parasites pose to the human population. The associations parasites form with their hosts are responsible for a number of the worst emerging diseases impacting global health today, including Ebola, HIV, and malaria. Macro-scale coevolutionary research aims to analyse these associations to provide further insights into these deadly diseases. This approach, first considered by Fahrenholz in 1913, has been applied to hundreds of coevolutionary systems and remains the most robust means to infer the underlying relationships which form between coevolving species. While reconciling the coevolutionary relationships between a pair of evolutionary systems is NP-Hard, it has been shown that if dating information exists there is a polynomial solution. These solutions however are computationally expensive, and are quickly becoming infeasible due to the rapid growth of phylogenetic data. If the rate of growth continues in line with the last three decades, the current means for analysing dated systems will become computationally infeasible. Within this thesis a collection of algorithms are introduced which aim to address this problem. This includes the introduction of the most efficient solution for analysing dated coevolutionary systems optimally, along with two linear time heuristics which may be applied where traditional algorithms are no longer feasible, while still offering a high degree of accuracy 91%. Finally, this work integrates these incremental results into a single model which is able to handle widespread parasitism, the case where parasites infect multiple hosts. This proposed model reconciles two competing theories of widespread parasitism, while also providing an accuracy improvement of 21%, one of the largest single improvements provided in this field to date. As such, the set of algorithms introduced within this thesis offers another step toward a unified coevolutionary analysis framework, consistent with Fahrenholz original coevolutionary analysis model
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