A PIPELINED BASED FPGA IMPLEMENTATION

OF A GENETIC ALGORITHM

Nonel Thirer

HIT – Holon Institute of Technology, 54 Golomb Street, Holon, Israel

Keywords: Genetic Algorithm, Pipeline, Resources, Subpopulations, Flexible Implementation.

Abstract: Many problems common to the electrical and electronics field can be solved by finding a target function and

its minimum or maximum. For such problems, usually an analytical solution is not implementable, and

therefore iterative algorithms are used. One such efficient algorithm is the Genetic Algorithm (GA). The GA

imitates the biological evolution process, finding the solution by implementing the “natural selection”

principle, which asserts that the strong has higher chances to survive. The GA is an iterative procedure

which operates on a population of individuals called "chromosomes" or "possible solutions" (usually

represented by a binary code) and performs several processes on the population individuals, in order to

produce a new population - the same as in the biological evolution. Using the algorithm on large populations

requires substantial hardware resources. Also, naturally, the amount of time necessary to reach a solution

increases, due to the greater number of iterations needed. In this paper, we present an FPGA pipelined based

method designed to implement a GA, which provides a high-speed solution for large populations, with a

minimum of resources. This outcome is obtained by a procedure which operates sequentially with parts of

the population. In addition, an immigration unit is defined to provide an efficient communication between

these parts in different iterations. Moreover, some possible solutions to improve our method are analyzed.

1 INTRODUCTION

The principal steps of the GA (Affenzeller, 2009)

are:

Population initialization – build a random initial

bank of chromosomes.

Fitness calculation – calculate the “Fitness score”

for each chromosome in the current chromosome

bank. The Fitness score of a chromosome is the

amount of adjustments needed to solve the main

problem. If a chromosome that solves the main

problem is found, the GA is stopped.

Selection – the GA chooses several

chromosomes from the current bank, with respect to

their calculated fitness score.

Crossover – two or more of the chosen

chromosomes randomly switch their bits and build

up a new chromosome.

Mutation – one random bit is toggled in the

newly built chromosome.

The stages are repeated until a new chromosome

bank (population) is built and the process continues

with the new population.

The software implementations of the algorithm

convert these steps to a multi phase process. The

FPGA (Field Programmable Gate Array)

implementations provide special hardware blocks for

every phase and also general blocks, which are

common to many phases (Mao, 1999). Evidently,

this implementation on large populations requires

substantial hardware resources. In addition, the time

needed until arriving to a solution increases, due to a

greater number of iterations. The use of a parallel

architecture improves the speed but requires many

resources (Tatsuhiro, 2006).

Problem–specific and also problem–independent

designs and implementations are available (Tiago,

2004).

A good algorithm must provide a high speed

solution in parallel to resources’ optimization for a

problem - independent FPGA implementation.

2 A NOVEL IMPLEMENTATION

ALGORITHM

We proposed a novel implementation algorithm

based on a pipelined system working with parts

(“subpopulations”) of the entire populations. These

343

Thirer N..

A PIPELINED BASED FPGA IMPLEMENTATION OF A GENETIC ALGORITHM.

DOI: 10.5220/0003687703430345

In Proceedings of the International Conference on Evolutionary Computation Theory and Applications (ECTA-2011), pages 343-345

ISBN: 978-989-8425-83-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

subpopulations are not isolated and the best

individuals are transferred between subpopulations.

The initial population is divided to n

subpopulations and a GA multi stage process is

complete for each subpopulation by an m phases

pipeline procedure.

To process all these subpopulations, instead of

using a parallel configuration with n pipelines

(Tatsuhiro, 2006) we propose a sequential procedure

by using this m phases pipeline. Thus, only

resources for a single pipeline are necessary.

Evidently, for best results, the initial population

must be divided to n subpopulations, where n = m.

By using a single pipeline, the working time is

longer than using parallel pipelines, but the

sequential operation with the subpopulations

increases the throughput.

A “transfer” unit is introduced, to permit an

efficient communication between the subpopulations

in different iterations and also to improve the fitness

score of each subpopulation. In our method, the

transfer is provided by added to each subpopulation

the best p members of a previously evaluated

subpopulation. This, except in the case of the first

two subpopulations in the initialization phase (in this

case p members of another subpopulations must be

added randomly).

In each procedure, the Fitness block will provide

the best p candidates to be added to the next

subpopulation procedure.

2.1 Implementation of a Four Stages

In the four stages GA, the pipeline contains four

special blocks:

M - Working memory (including the transfer

unit), E - Evaluation, S - Selection, CM - Crossover

and Mutation.

P0 is our initialization population. We divide it

to four subpopulations: P0_1, P0_2, P0_3, P0_4.

P0_11, P0_21, P0_31, P0_41 are the new

subpopulations after the first iteration,

P0_12, P0_22, P0_32, P0_42 are the new

subpopulations after the second iteration and P0_1x,

P0_2x, P0_3x, P0_4x are the new subpopulations

after the x-th iteration.

In this case, the four stages pipeline procedure

will work in the following mode:

Time Pipeline Blocks

M E S CM

1P0

2P0

est to M

3P0

3 P0_2 P0_1

+best P0

est to M

4P0

3P0

2 P0

+best P0_2

est to M

5P0

11 P0

4P0

3 P0

+best P0

est to M

6 P0_21 P0_11 P0_4 P0_3

+best P0

est to M

7P0

31 P0

21 P0

11 P0

+best P0

est to M

8P0

41 P0

31 P0

21 P0

+best P0

est to M

And so forth ad so on.

By example, the system status at time t7 is as

follows:

The Memory Block M contains P0_31 which is

the first generation of the third subpopulation (P0_3

which already passed all the pipeline stages) and the

best individuals of the previous evaluated P0_11.

The Evaluation Block E evaluates the P0_21

subpopulation (fitness calculations) to provide data

for the Transfer unit and also for the Selection

Block. Obviously, if a chromosome that solves the

main problem is found, the process is stopped.

The Selection Block S chooses the chromosomes

of the P0_11 subpopulation to be the next parents.

The Crossover and Mutation Block CM provides

a new generation (P0_41) of the P0_4

subpopulation.

In this way at time t5 the entire population is

evaluated and a new generation is obtained at time t4

to t7.

2.2 General Blocks

The implementation of the GA requires also general

blocks used by two or more special blocks. Thus, a

major component of the hardware architecture of the

GA is the source of pseudo randomly noise. Our

pseudo randomly binary number generator (PRBG),

based on LCA (Linear Cell Automata) is used to

select the parents groups (from the fitness output)

and also (in parallel) to provide the bits for the

crossover and mutation phase (Godkin, 2010).

The PRGB is based on 2q+1 flip-flops and their

outputs (0 to 2q) provide a 2q+1 pseudo random bit

sequence (PRBS). The GA uses 2q sequences: bit 0

up to bit 2q, bit one up to bit 2q and bit 0, and so on.

Those 2q+1 bits sequences are then divided into

ECTA 2011 - International Conference on Evolutionary Computation Theory and Applications

344

smaller sequences, of q bit each, using the same

principle, bit 0 up to bit q-1, bit 1 up to bit q, and so on.

In this manner, the PRGB provides the GA with

many random numbers (values: 0 to 2



- 1) at each

iteration.

2.3 Implementation Parameters

The hardware implementation of the algorithm on

any specific FPGA device requires the user to define

some hardware parameters, such as:

• the size of the population members (n)

• the number of bits of each population

individual

• the size of the immigration population - the

number of the best members (p)

• the size of the PRNG (2k+1)

• the maximum number of generations (gn)

• the precision used in fitness estimation

• the number of bits of the crossover

• the number of bits and the probability of the

mutation

Careful and precise definitions of the above

mentioned parameters will provide us with a flexible

implementation. The above hardware parameters

depend, of course, on the nature of the problem

needing a solution, and are restricted by the specific

FPGA chip characteristics.

2.4 Extended Solution

The presented algorithm, based on a pipelined

system working with “subpopulations” of the entire

populations, can be adapted to any s-stages Genetic

Algorithm (working, for example, with memory and

transfer as separate phases and also with crossover

and mutation as separate phases) by dividing the

initial population to s subpopulations.

The flip-flop array of the PRNG could easily be

expanded if the amount of random numbers supplied

to GA is not enough to make all the necessary

calculations during a single iteration. Also, the

PRNG component can be defined and implemented

by using other algorithms, for example LFSR -

linear feedback shift registers (Nedjah, 2007).

Another option is using a predefined component.

As it is usually the case with genetic algorithm

implementations, there are no guarantees that using

these methods will provide a better solution in the

next generation. A simple comparator unit can be

added to detect and store the best solution found to a

specific point in time, and after gn generations the

user may use this best solution, if a chromosome that

solves the main problem wasn’t found.

3 CONCLUSIONS

The above portrayed method provides a flexible and

compact implementation of a given problem, using a

genetic algorithm and an FPGA device.

The method allows us to work with a large size

population by using a small amount of resources. It

does so by dividing the initial population into

“subpopulations”. The “transfer” step provides the

necessary interconnection between the members of

the entire population.

The presented pipeline organization permits an

implementation of a four stages algorithm. For a five

or more stages algorithm the pipeline must be

reconsidered and adapted to usage as a five or more

stages pipeline.

As it is often the case with genetic algorithm

implementations, the runtime can’t be exactly

calculated. However, it is certain that the time

needed to accomplish a single iteration using a

pipeline procedure is longer that without pipeline,

but a considerable increase of the throughput will be

obtained.

To provide a flexible implementation, some

hardware parameters should be defined prior to the

FPGA implementation. Also, external defined

components (as PRBG) can be used.

REFERENCES

M. Affenzeller A. O., 2009. Genetic Algorithms and

Genetic Programming – Modern Concepts and

Practical Applications, CRC Press, USA.

N. Nedjah, L. M. Mourelle, 2007. An efficient problem-

independent hardware implementation of genetic

algorithms., Neurocomputing 71, p.88-94.

Mao F. So, Angus Wu, 1999. FPGA Implementation of

Four –Step Genetic Search Algorithm., Electronics,

Circuits and Systems, Proc. of ICECS '99, vol.2 p.11

43-1146.

Tatshuito Tachibana A. O., 2006. Flexible Implementation

of Genetic Algorithms on FPGA, Proc. of the

ACM/SIGDA 14th International Symposium on Field

Programmable Gate Arrays, FPGA, USA, February

22-24, 2006, 9 pages

Tiago Carvalho Oliveira, Valfredo Pilla Jr, 2004. An

Implementation of Compact Genetic Algorithm on

FPGA for extrinsic evolvable Hardware, IEEE Tran-

sactions on Evolutionary Computation, p.1143-1146.

Godkin Andrey, Nonel Thirer, 2010. A FPGA

Implementation of Hardware Based Accelerator for a

Genetic Algorithm, Proc. of IEEE 26-th Conv. of

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