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AbstractRecently, Engineering has complicated limits in manyfields. Large-scale optimization problems are very hard to get an

optimum solution in short arithmetic time. In this case, we use

evolutionary algorithm as a main mechanism that can solve theproblems in short arithmetic time. The evolutionary Algorithm is used

as an important mechanism that can solve these problems in suitable

arithmetic time. Mask's creation has become a basis for DNA

computing using GA(Genetic Algorithm); it makes a high propensity

for randomization and suitable masks. However, software simulation

requires much simulation time. In this paper, we propose a hardware

mask creation system for DNA computing using GA and compare it

with a software model.

KeywordsDNA Computing, Genetic Algorithm, Mask, Randomnumber generator

I. INTRODUCTION

NA computing has problems. It takes a long time to make

good compatibility masks with GA. For better

compatibility, we need to use a genetic algorithm that is an

evolutionary algorithm to apply DNAC. By doing so, we can

speed up the rates of generation. Further, since hardware

implementation of the genetic algorithm will solve the problem

of the generation of mask that needs to be made an executed in

real-time, it is important that the genetic algorithm needs to

execute repeatedly to extend over several generations.

There are two problems when we simulate software

implementation for DNAC. The process speed is slow because

it needs to compare all of the data. We can solve this problem by

using hardware implementation that uses parallel processing.This speeds up the process. We use the process to execute

simple counts perfectly. But it is hard to use parallel processing

Sang-Seol Lee, Jae-Yeon Song, Kyu-Yeul Wang, Byung-Soo Kim,

Jae-Young Choi, Seong-Seob Shin are with the Inha University, Incheon, South

Korea ( e-mail : [email protected], o [email protected],

[email protected], [email protected], [email protected],

[email protected], ).

Dong-Sun Kim is with the Department of Advanced Mobile Technology

Research Center, Korea Electronics Technology Institute, Korea. (e-mail:

[email protected]).

Duck-Jin Chung is currently a professor at the school of Information and

Communication Engineering of INHA University, Incheon, Korea.

(corresponding author to provide phone: +82-32-874-1663; e-mail:[email protected]).

when we need to implement huge DNA with GA. When we

make the mask using the random method, it causes some parts of

the mask to have a defect. We aim to solve this problem. We use

the genetic algorithm in the way of the survival of the fittest.

Therefore, we can solve the problems and improve this

processing.

In this paper, we simulate genetic algorithm by using Visual

studio to get the result about performance improvement. We

implement genetic algorithm by using Verilog-HDL and C

programming language to know speed rates. The rest of the

paper is organized as follows. We describe the genetic

algorithm in terms of the definition and the requirements for

parallel processing. Also, in section 2, the proposed genetic

algorithm architecture is discussed in terms of a random number

generator module, the way of selection, crossover block,

mutation block, and the hardware implementation for

compatible function blocks. In section 3, we present the resultthat is compatible with the software simulation and the hardware

simulation in the genetic algorithm. The paper concludes with a

discussion of DNA computing using the genetic algorithm in

section 4.

II. IMPLEMENTATION OF GAFOR MASK CREATION

A. What is the Genetic algorithm?

The genetic algorithm has individual of high fitness. The high

quality of individual will survive more than others. This optimal

algorithm is based on law of the natural selection. It is widelyused in area of pattern recognition, neural network, voice

recognition and robotics. The genetic algorithm was introduced

by John Holland. The algorithm encodes a lot of individual

characteristics such as genetic organization. After that, it

evolves a parallel way using a genetic operator. Therefore, the

parallel way uses the techniques of probability search to reach

the optimized state.

Figure.1 depicts a flow chart of the genetic algorithm. It

describes the procedure. The many masks represent the type of

chromosome and then the population of generated

chromosomes initialize. The randomly initialized individuals

are evaluated by the algorithms problem-solving capability.

The individuals' fitness is decided through this evaluation

Mask Creation for DNA Computing

Using Genetic Algorithm

Sang-Seol Lee, Jae-Yeon Song, Kyu-Yeul Wang, Byung-Soo Kim, Jae-Young Choi, Seong-Seob Shin,

Dong-Sun Kim and Duck-Jin Chung

D

World Academy of Science, Engineering and Technology 50 2009

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process. When genes generate children, the survival of the fittest

is used. Individuals are selected through a fitness-based process,

where fitter solutions are typically more likely to be selected.

This selection procedure is repeated until it reaches the end

condition

Fig. 1 the flow chart about genetic algorithm

B. Parallel processing

The genetic algorithm is the principle of the naturalprogression of evolution. GA makes population to evolve

continuously and the population converges into optimum

solution. The children are produced by genetic operation:

selection, crossover, mutation and reproduction. It is a simple

theory and easy to apply. GA has a drawback, which is that it

makes the process too slow when it is simulated by software in

computers. We need parallel processing that runs evolutionary

arithmetic using different several groups to overcome the speed

problem.

C. Operation

The random number generator generates random variables to

make the mask in each address. We calculate the fitness of themask to save a memory. Among the masks, we choose the best

mask to have high fitness. After that, the parent generation is

initialized. We generate each address by a random number

generator. Each address aims at relevant data A and data B. The

data sets are parent A and parent B. The crossover chooses a

random point that is generated by a random number generator to

exchange genetic traits of parent A and B. We operate the

mutation module. It depends on the probability of mutation. We

got the result that the mask is convergence from one hundred

individuals..

TABLE I

THE PSEUDO-CODE OF THE PROPOSED GA FOR MASK CREATION

functionGA for Mask Creation(population, FITNESS) returnsan

individual

input:population, a set of individuals

FITNESS, a function that measures the fitness of an

individual

repeat

Operation parameter random number generator

parentsSELECTION(population,FITNESS)

population Crossover & Mutation (parents)

untilsome individual is fit enough

returnthe best individual inpopulation, according to FITNESS

Fig. 2 the flow chart about genetic algorithm

D. Selection

The selection module selects the object of parents to make

two child buffers. Data transmission consists of handshaking

protocol between memory and the random number generator.

The right block selects the object of parents for crossover and

mutation. The left block has the whole control. The roulette

wheel selection process is used in our mask creator. As shown in

Fig. 3, after it selects two parents in memory, and then the

parents pass to two child buffer in one clock, it contributes to

both utilized parents for fast arithmetic and reduction of

hardware size.

.

Buffer1-1 Buffer1-2

Random value

Generator

crossover point

Mutation rate

Mutation point

Mutation change

value

Buffer2-1 Buffer2-2

Mutation

Controller

Crossover Controller

Parent Buffer

Fig. 3 operation block


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E. Crossover & Mutation module

This module is designed so that two operations may be

performed continuously. In case of crossover, it uses 1- point

crossover method. Mutation uses a single point mutation

method. Crossover and mutation are combined. As shown in Fig.

3, the block is hardware structure of crossover and mutation.

Parents receive the intersection points in the random number

generator; each pair undergoing crossover and mutation. The

mechanics of crossover and mutation are simple, involving the

random number generator. Nonetheless, this module is efficient

in optimization.

F. Fitness function module

To test the fitness function for DNA computing in masks, we

use decision block with digit recognition.[11]

We compare the original train data and masks. If the train

data and mask are the same, we increase the fitness value and

store it together with mask in memory.

Fig 4. Decision Block & Fitness

G. Selection bits (Mask bits)

In this architecture, selection bits and the number of parallel

processing units are 31 and 1000. In software and hardware

simulations, we archived mask best accuracy on 31 bits (fig 5).

Fig 5. Accuracy according bits size

TABLE II

FUNCTION PROPERTIES

Function Quantity

Selection Roulette wheel

Crossover One-point crossover

Mutation Single port mutation , 0.05# of mask bits 31 bits

Modified Gollmann casecade(HW)Random

number

generatorrPseudo random number(SW)

III. R ESULTS

The aim of this paper is a mask creation for DNA computing

using genetic algorithm. The fitness of the first mask is shown in

Fig.6 We calculate the value of fitness from the random number

generator and then we save the value of fitness to the memory.

We repeat this process 100times. It is the first generated mask.

The function's fitness is large at GA. Therefore, we need more

time to create the mask by using GA than by using the ordinary

way.

We simulated this system by using software. At that time, we

needed over 21 hours to simulate the system by using a

computer that is dual core2 6600, and ram 2G. On the other

hand, we can get a better result by using hardware simulation. In

this case, we just need about 200 seconds to simulate The

system on the same computer system. It improves the processing

time by 380 times. Also, we ensure the result of more than

99.8% accuracy from self-matching and about 93% accuracy

from test pattern matching by using DNA computing.

Fig 6. 1st mask fitness

Fig 7. The matching result simulated by ModelSim.


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Fig. 8. Accuracy results

IV. CONCLUSION

In this paper, we can save mask generation time by using GA

that implements parallel processing and hardware

implementation. We can apply this system to real-time because

the proposed way has decreased the calculation time and the

number of iterations of genetic algorithm. In addition, we can

put trains in this system when we need to use a new pattern. We

can get good results from the proposed system.

REFERENCES

[1] J. H. Holland, "Adaptation in Natural and Artificial Systems," Univ. of

Michigan Press, Ann Arbor, 1975.

[2] Jenkins, Robert J. "ISAAC". http://burtleburtle.net/bob/rand/isaac.html

[3] Melanie Mitchell, An Introduction to Genetic Algorithm, 1997

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Search in Hybrid Genetic Algorithms, 2001

[5] D. E. Goldberg, ed, Genetic Algorithm in search, Optimization,andMachine Learning, Addison-Wesley, 1989.

[6] K. Dejong, An analysis of behavior of a class of genetic adaptive

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[7] M. Srinivas, and L. M. Patnaik, Adaptive Probabilities of Crossover and

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[8] L. B. Booker, D. E. Goldberg, and J. H. Holland, Classifier Systems and

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1987.

[9] Philips Thrift, Fuzzy Logic Synthesis with Genetic

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[10] Byung-Tak Zhang and Joo-Kyung Kim, DNA Hypernetworks for

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[11] Byung-Soo Kim, Joo-Kyung Kim, Oh Hyuk Kwon, Seung Kon Hwang,Jae-Yeon Song, Byoung-Tak Zhang, Chong Ho Lee, Jaehyun Park, and

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