A genetic algorithm for the minimum hitting set

Cendic-Lazovic, Bojana

doi:10.5937/spsunp1402107c

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…However, in this Section we compare FLEXDIAG with an evolutionary algorithm inspired by [7,30]. The evolutionary algorithm has been build with the jenetics framework for Java 6 leaving all parameters default and fixing the process to 500 generations.…”

Section: Comparing Flexdiag With Evolutionary Algorithmsmentioning

confidence: 99%

Anytime diagnosis for reconfiguration

et al. 2018

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Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and SelectionArts, Graz, Austria J Intell Inf Syst diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

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Section: Comparing Flexdiag With Evolutionary Algorithmsmentioning

confidence: 99%

Anytime diagnosis for reconfiguration

et al. 2018

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“…Since the Hitting Set problem is NP‐hard, there exist some solutions that try to circumvent this problem, either by imposing restrictions, such as with non‐polynomial exact algorithms, or by using heuristics and approximations . More recently, Ruchkys and Song proposed sequential and parallel approximation algorithms to solve HSP, tailored for systems biology problems.…”

Section: Related Workmentioning

confidence: 99%

“…Since the Hitting Set problem is NP-hard, 15 there exist some solutions that try to circumvent this problem, either by imposing restrictions, such as with non-polynomial exact algorithms, 16 or by using heuristics and approximations. 17,18 More recently, Ruchkys and Song 5 proposed sequential and parallel approximation algorithms to solve HSP, tailored for systems biology problems. Steinbach and Postohoff 19 proposed an exact, exhaustive search driven algorithm using single GPU (Graphics Processing Units) to determine the minimum cover of Boolean expressions, which is dual to the HSP.…”

Section: Related Workmentioning

confidence: 99%

A hybrid CPU‐GPU‐MIC algorithm for minimal hitting set enumeration

Carastan-Santos

Martins-Jr

Song

et al. 2018

Concurrency and Computation

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Summary We present a hybrid exact algorithm for the Minimal Hitting Set (MHS) Enumeration Problem for highly heterogeneous CPU‐GPU‐MIC platforms. With several techniques that permit an efficient exploitation of each architecture, low communication cost, and effective load balancing, we were able to enumerate MHSs for large instances in reasonable time, achieving good performance and scalability. We obtained speedups of up to 25.32 in comparison with using two six‐core CPUs and we also enumerated MHSs for instances with tens of thousands of variables in less than 5 hours. We also evaluated our algorithm with a real‐world driven dataset, and with a large CPU‐GPU cluster, we unprecedentedly enumerated in parallel large minimal hitting sets of this dataset in less than 8 hours. These results reinforce the statement that heterogeneous clusters of CPUs, GPUs, and MICs can be used efficiently for high‐performance computing.

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