Fast Random Integer Generation in an Interval

Lemire, Daniel

doi:10.1145/3230636

Cited by 42 publications

(24 citation statements)

References 28 publications

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“…A future work will be the design in the hardware of DE, which should include the random number generator that can be optimized to use directly the generated bits without FP divisions, as is suggested in [10]. This idea of this design also could be used in software within each core of a GPGPU.…”

Section: Datamentioning

confidence: 99%

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Differential Evolution under Fixed Point Arithmetic and FP16 Numbers

Fraga

2021

MCA

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In this work, the differential evolution algorithm behavior under a fixed point arithmetic is analyzed also using half-precision floating point (FP) numbers of 16 bits, and these last numbers are known as FP16. In this paper, it is considered that it is important to analyze differential evolution (DE) in these circumstances with the goal of reducing its consumption power, storage size of the variables, and improve its speed behavior. All these aspects become important if one needs to design a dedicated hardware, as an embedded DE within a circuit chip, that performs optimization. With these conditions DE is tested using three common multimodal benchmark functions: Rosenbrock, Rastrigin, and Ackley, in 10 dimensions. Results are obtained in software by simulating all numbers using C programming language.

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Section: Datamentioning

confidence: 99%

“…The DE in Algorithm 1 can be improved by using a random integer number generator as the one described in [10], which does not use divisions or FP numbers. This idea could improve the algorithm in line 6 (to generate three numbers in the interval [1, µ], and in line 7 where another random integer number is generated in the interval [1, n].…”

mentioning

confidence: 99%

Differential Evolution under Fixed Point Arithmetic and FP16 Numbers

Fraga

2021

MCA

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“…The random generator used was the default used in the RBG system: std::mt19937 together with the Lemire's selection method (Lemire 2019;Kowalski et al 2020).…”

Section: Environmentmentioning

confidence: 99%

Split Moves for Monte-Carlo Tree Search

Kowalski¹,

Mika²,

Pawlik³

et al. 2021

Preprint

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In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents' playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents' strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single-as well as multi-action games.

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“…Further, an R-wrapper is available through GitHub. The tool leverages a PCG random number generator that provides a simple, fast, and space-efficient algorithm for generating random numbers with high statistical quality [16]. The tool uses a Monte Carlo approach for randomly generating somatic mutations while considering the observed frequency of a preselected reference genome.…”

Section: Tool Implementationmentioning

confidence: 99%

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

2020

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Background Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. Results Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes. Conclusions SigProfilerSimulator’s breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.

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Fast Random Integer Generation in an Interval

Cited by 42 publications

References 28 publications

Differential Evolution under Fixed Point Arithmetic and FP16 Numbers

Differential Evolution under Fixed Point Arithmetic and FP16 Numbers

Split Moves for Monte-Carlo Tree Search

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

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