A Genetic Algorithm with Restart Strategy for Solving Approximate Shortest Vector Problem

Luan, Luan; Gu, Chunxiang; Yan, Zheng

doi:10.1109/icaci49185.2020.9177756

Cited by 1 publication

(12 citation statements)

References 29 publications

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“…In our experiments, we averaged the running time over 100 trials for each dimension; hence, the dimensions covered by our experiments were limited to between 40 and 90. These dimensions are low compared to those in [15,16], where the experiments, whose running time data were not averaged, as stated above, reached dimensions of 118 and 100, respectively. In our future work, we will concentrate on proceeding to higher dimensions.…”

Section: Introductionmentioning

confidence: 74%

“…As we will explain further in Sect. 4, Luan et al showed that the GA can automatically optimize one of the parameters that affect the size of the search space used in [6] although this is not explicitly claimed in [16]. We believe that this is one of the major contributions of Luan et al's work.…”

Section: Luan's Algorithmmentioning

confidence: 90%

“…Chromosome length for a given dimension For a given lattice dimension, Luan's chromosomes are shorter than Ding's because unlike the former, the latter include entries (bits) for signs and hence are extended by the number of such entries. Mutation It is known that the initial entries of a short lattice vector in the form of either a y-sparse representation or an NNR are all 0 (refer to [6,15,16]). This means that because our aim is to find a short lattice vector, we do not need to explicitly consider these initial entries.…”

Section: A Qualitative and Experimental Analysis Of Ding's And Luan's...mentioning

confidence: 99%

“…The works by Ding et al [15] and Luan et al [16] are examples of the application of GAs to the SVP. To obtain an efficient GA for solving the SVP, it is necessary to consider the following basic questions:…”

Section: Introductionmentioning

confidence: 99%

“…The running time of a GA changes significantly in every trial. However, the running time data in [15,16] were not averaged, and hence, the experimental results in [15,16] do not seem to be reliable. In our experiments, we averaged the running time over 100 trials for each dimension; hence, the dimensions covered by our experiments were limited to between 40 and 90.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improving genetic algorithms for solving the SVP: focusing on low memory consumption and high reproducibility

Fukase

Kaminaga

2022

Iran J Comput Sci

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The shortest vector problem (SVP) is absolutely essential in lattice-based cryptography. In this paper, we significantly improve genetic algorithms (GAs) for solving the SVP. GAs, which are simple and powerful optimization techniques, that have the potential to eliminate the limitations of the existing fast SVP algorithms. We improve the entire phase of the GA construction. Our proposed method is based on the concept of low memory consumption and high reproducibility, and this is the main and significant difference between our algorithm and the other SVP algorithms. Our contributions are twofold. First, we developed a new GA for solving the SVP and achieved a considerable improvement in the running time performance. Second, we interpreted certain genetic operations, such as mutation and crossover, in the context of lattices, which has not been done in previous studies. The general result of this paper is that we showed the potential of GAs in the field of lattices.

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Section: Introductionmentioning

confidence: 74%

Section: Luan's Algorithmmentioning

confidence: 90%