CN108108152A - A kind of real random number generator automatic optimization method - Google Patents
A kind of real random number generator automatic optimization method Download PDFInfo
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- CN108108152A CN108108152A CN201611049403.0A CN201611049403A CN108108152A CN 108108152 A CN108108152 A CN 108108152A CN 201611049403 A CN201611049403 A CN 201611049403A CN 108108152 A CN108108152 A CN 108108152A
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Abstract
The present invention provides a kind of real random number generator automatic optimization methods.The real random number generator automatic optimization method is due to can be according to the difference of different process, choose the parameter of real random number generator, during process shifts, engineer the circuit of true random number need not be calculated and designed again, accelerate the cycle of product design and production.Meanwhile for the special screne of randomizer, population definition is redescribed, the improvement to genetic algorithm, crossover process is the intersection of chromosome, and non-replaceable gene；Also, the mutation process of chromosome is divided into two steps, adds the variation situation of chromosome quantitative, moreover, this automatic optimization method improves the convergence rate of genetic algorithm, so as to achieve the effect that real random number generator area and power consumption optimum.
Description
Technical field
The present invention relates to real random number generator technical field more particularly to real random number generator automatic optimization methods.
Background technology
Real random number generator (TRNG) is to generate random sequence using the randomness of the physical phenomenon of nature.Such as
Shown in Fig. 1, for existing randomizer schematic diagram, wherein Fig. 1（a）, it is a kind of based on ring oscillator realization method
Randomizer, the randomness of this ring oscillator are decided by the random of the thermal noise of phase inverter on loop oscillator first
Property, next is decided by the number of the loop of ring oscillator, the number of phase inverter on loop.Therefore when same realization method
Randomizer when replacing integrated circuit design process, since the thermal noise performance of device is different, causes true random number to be sent out
The randomness of raw device is different.Recurrent problem is that real random number generator only replaces device, is changed without the feelings of assembled scheme
Under condition, in the product under A techniques, real random number generator can provide more reliable random number, but when being transplanted to B techniques,
Requiring for randomness cannot be met.The deviation of randomness caused by the deviation between different process was not being transplanted
Considered in journey.In order to increase the robustness of true random number, it will usually which selection is continuously increased loop quantity and changes on loop
The mode of the quantity of phase inverter improves randomness, such as Fig. 1（b）Shown, still, although this mode improves randomizer
Entropy（That is randomness）, but cause the waste of larger area and power consumption.Therefore, it is necessary to a kind of methods can be given at not
With under process condition, any combination mode of real random number generator it is good can either to reach randomness, additionally it is possible to accomplish area
With the designing scheme of power consumption optimum.
The content of the invention
It is insufficient present in for the above-mentioned prior art, it is an object of the present invention to provide a kind of real random number generator certainly
Dynamic optimization method, this method automatically according to the difference of different process, can choose the parameter of real random number generator, provide satisfaction
It is required that optimal case.
In order to reach above-mentioned technical purpose, the technical solution adopted in the present invention is：
A kind of real random number generator automatic optimization method, the automatic optimization method are as follows：
The first step：Initialization of population, content include：（1）The number of total group；（2）The maximin of chromosome in population；（3）
The maximin of genic value；（4）Iterations maximum；（5）Crossing-over rate and aberration rate；（6）Random number is initially in total group
Primary individual；
Second step：Generation heredity is carried out, therefore iterations adds 1；
3rd step：It calculates in this generation, individual fitness function；
4th step：According to the value of fitness function, made choice using roulette algorithm；
5th step：Intersected using the individual selected；
6th step：The variation of chromosome quantitative gene numerical value is carried out using the individual after selection and intersection；
7th step：Individual after being made a variation using the 6th step carries out the variation of chromosome quantitative；
8th step：Judge whether there are 10 round optimal values not change；If optimal value 10 is taken turns unchanged, algorithm terminates；
Otherwise then carry out the 8th step；
9th step：Judge whether the round is more than or equal to maximum iteration, if it is, algorithm terminates；If not then sheet
Terminate for genetic algorithm, return to second step.
Preferably, in the real random number generator automatic optimization method, the population is defined as：Assuming that population quantity is
M, each individual have n chromosome, and each chromosome is there are one gene, and numerical value q, then each individual is expressed as in total group：Xm
=[q1, q2, q3 ... qn].
Parameter of the present invention since real random number generator according to the difference of different process, can be chosen, in process shifts
In the process, engineer the circuit of true random number need not be calculated and designed again, accelerate the cycle of product design and production.
Meanwhile for the special screne of randomizer, population definition, the improvement to genetic algorithm are redescribed, crossover process is
The intersection of chromosome, and non-replaceable gene；The mutation process of chromosome is divided into two steps, adds the variation feelings of chromosome quantitative
Condition, moreover, this automatic optimization method improves the convergence rate of genetic algorithm, so as to reach real random number generator area and
The effect of power consumption optimum.
The present invention will be further described with reference to the accompanying drawings and detailed description.
Description of the drawings
Fig. 1 is existing randomizer schematic diagram.
Fig. 2 is the real random number generator automatic optimization method flow chart that the present invention is embodied.
Specific embodiment
Referring to Fig. 2, the real random number generator automatic optimization method flow chart being embodied for the present invention.Wherein, population
It is defined as：Assuming that population quantity is m, each individual has n chromosome, there are one each chromosomes gene, numerical value q, then always
Each individual is expressed as in group：
Xm=[q1, q2, q3 ... qn].
（1）Fitness function：Two optimization aims include randomness and power consumption.
The function of randomness is R (xm), and different oscillator loop models has different randomness functions, but should ensure that
Randomness is bigger, and the value of random function is bigger；The function of randomness is represented in the technical program using entropy function.
Prob ue ：The probability that the output of random number oscillator is 1.
（2）Power consumption function is P (xm), and the device in different process storehouse is different, and the calculation of power consumption function is also different,
But it should ensure that power consumption is bigger, the value of power consumption function is bigger.
（3）Fitness function：Fitness function is higher, and individual fitness is better, according to the situation of this programme, it should be
Randomness is better, and the value of the smaller individual adaptation degree function of power consumption is bigger.Therefore fitness function is：
f(xm) = z R(xm)/P(xm)
Wherein, z is turning towards parameter so that the maximum of f (xm) is 1.
（4）Selection：There are many method for selecting individual, roulette etc., here not specific certain scheme of finger；
The step of wheel disc bet method：
A：Calculate the summation of the fitness of all individuals in group；
B：The sum of the probability that each individual is genetic in next-generation group, whole probability values is calculated as 1；
C：Calculate the cumulative probability of each individual；
D：The random number r between one 0 to 1 is generated, if r<Q [1] then selects individual 1, otherwise, selects individual k so that：q[k-
1]<R≤q [k] is set up.
（5）Intersect：The mode chiasmatypy that the two former generation's chromosome single-points intersected intersect.Generate random number
R1, r2, r3 work as r1<P (selection rate), above-mentioned（4）In the individual of selection, intersection, Cross reaction body are proceeded by from r2 positions
Length is r3；
Such as：The ith and jth individual in the n-th generation is selected for exchanging, and the ith and jth individual for obtaining for the (n+1)th generation is as follows
It is described：
Xn,i=[q1,q2,q3…qr2…qr2+r3…qn] Xn+1,i= [q1,q2,q3…pr2…pr2+r3…qn]
Xn,j=[p1,p2,p3…pr2…pr2+r3…pn] Xn+1,j=[p1,p2,p3…qr2…qr2+r3…pn]
（6）Variation：Due to the particularity of scheme, the variation of the program is divided into two steps：
A:The mode to make a variation into offspring's chromosome single-point of row variation changes certain chromosome.Random number r1, r2 are generated, works as r1<q
(aberration rate), in the individual after the 5th step is intersected, the r2 positions in individual carry out chromosomal variation；For example, the n-th generation
I-th of individual is selected for variation A, and i-th of body for obtaining for the (n+1)th generation is as described below：
Xn,i=[q1,q2,q3…qr2…qn] Xn+1,i=[q1,q2,q3…mr2…qn]
B:Quantity into offspring's chromosome of row variation increases.Generate random number r1, r2；Work as r1<Q (aberration rate), the variation
Body increases a chromosome, and the value for increasing chromogene is r1>1-q (aberration rate), variation individual reduce by a chromosome,
The value of the gene of chromosome is 0, increases chromosome.For example, i-th of individual in the n-th generation is selected for variation B, increase by one
Chromosome, i-th of body for obtaining for the (n+1)th generation are as described below：
Xn,i=[q1,q2,q3……qn] Xn+1,i=[q1,q2,q3……qn,qn+1,]
I-th of individual in the n-th generation is selected for variation B, reduces by a chromosome, obtains the following institute of i-th of body in the (n+1)th generation
It states：
Xn,i=[q1,q2,q3……qn] Xn+1,i=[q1,q2,q3……qn-1,0]。
From the above, it can be seen that due to the improvement to genetic algorithm, crossover process is the intersection of chromosome, and non-replaceable base
Cause so that the mutation process of chromosome is divided into two steps, adds the variation situation of chromosome quantitative, significantly improves genetic algorithm
Convergence rate, so as to achieve the effect that real random number generator area and power consumption optimum.
The present invention is not limited to embodiment discussed above, more than the description of specific embodiment is intended to retouch
It states and illustrates technical solution of the present invention.The obvious conversion or replacement enlightened based on the present invention should also be as being considered
Fall into protection scope of the present invention；Above specific embodiment is used for disclosing the optimal implementation of the present invention, so that this
The those of ordinary skill in field can apply numerous embodiments of the invention and a variety of alternatives to reach the present invention's
Purpose.
Claims (2)
1. a kind of real random number generator automatic optimization method, which is characterized in that the specific steps of the automatic optimization method are such as
Under：
The first step：Initialization of population, content include：（1）The number of total group；（2）The maximin of chromosome in population；（3）
The maximin of genic value；（4）Iterations maximum；（5）Crossing-over rate and aberration rate；（6）Random number is initially in total group
Primary individual；
Second step：Generation heredity is carried out, therefore iterations adds 1；
3rd step：It calculates in this generation, individual fitness function；
4th step：According to the value of fitness function, made choice using roulette algorithm；
5th step：Intersected using the individual selected；
6th step：The variation of chromosome quantitative gene numerical value is carried out using the individual after selection and intersection；
7th step：Individual after being made a variation using the 6th step carries out the variation of chromosome quantitative；
8th step：Judge whether there are 10 round optimal values not change；If optimal value 10 is taken turns unchanged, algorithm terminates；
Otherwise then carry out the 8th step；
9th step：Judge whether the round is more than or equal to maximum iteration, if it is, algorithm terminates；If not then sheet
Terminate for genetic algorithm, return to second step.
2. real random number generator automatic optimization method as described in claim 1, which is characterized in that the population is defined as：
Assuming that population quantity is m, each individual has n chromosome, and each chromosome is there are one gene, numerical value q, then in total group each
Individual is expressed as：Xm=[q1, q2, q3 ... qn].
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111427541A (en) * | 2020-03-30 | 2020-07-17 | 太原理工大学 | Machine learning-based random number online detection system and method |
| CN111456958A (en) * | 2020-01-19 | 2020-07-28 | 追觅科技(上海)有限公司 | Fan rotating speed control method and computer readable storage medium |
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| US20030145289A1 (en) * | 2002-01-25 | 2003-07-31 | Anderson David M. | Method and system for reproduction in a genetic optimization process |
| CN104135363A (en) * | 2014-07-28 | 2014-11-05 | 河海大学 | Method of generating pseudo random number based on regular cellular automation |
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| US20030145289A1 (en) * | 2002-01-25 | 2003-07-31 | Anderson David M. | Method and system for reproduction in a genetic optimization process |
| CN104135363A (en) * | 2014-07-28 | 2014-11-05 | 河海大学 | Method of generating pseudo random number based on regular cellular automation |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111456958A (en) * | 2020-01-19 | 2020-07-28 | 追觅科技(上海)有限公司 | Fan rotating speed control method and computer readable storage medium |
| CN111427541A (en) * | 2020-03-30 | 2020-07-17 | 太原理工大学 | Machine learning-based random number online detection system and method |
| CN111427541B (en) * | 2020-03-30 | 2022-03-04 | 太原理工大学 | Machine learning-based random number online detection system and method |
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