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A new design of Arabic keyboard layout for convenient typing and effective use of keyboard is proposed. The design methodology is based on ergonomic criteria that evaluate and compares different keyboard layouts for optimal characters distribution. The proposed design criteria are simulated by a mathematical model that uses: 1) best distribution of the typing effort among the ten fingers of the typist; 2) accessibility of commonly used characters; 3) various other factors as constraint equations to reach an optimal keyboard layout that is ergonomically improved. This task is carried out using a genetic algorithm based optimisation framework. The new keyboard layout shows better ergonomic performance than the present keyboard being used in Arabic world with a 36.3% improvement.
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Int. J. Design Engineering, Vol. X, No. Y, xxxx 1
Copyright © 200x Inderscience Enterprises Ltd.
A new optimal Arabic keyboard layout using genetic
algorithm
E. Khorshid*, A. Alfadli and M. Majeed
Mechanical Engineering Department,
Kuwait University,
P.O. Box 5969 Safat, Kuwait
E-mail: Khorshid@kuc01.kuniv.edu.kw
E-mail: alfadlia1@yahoo.com
E-mail: mmajeed@kuc01.kuniv.edu.kw
*Corresponding author
Abstract: A new design of Arabic keyboard layout for convenient typing and
effective use of keyboard is proposed. The design methodology is based on
ergonomic criteria that evaluate and compares different keyboard layouts for
optimal characters distribution. The proposed design criteria are simulated by a
mathematical model that uses:
1 best distribution of the typing effort among the ten fingers of the typist
2 accessibility of commonly used characters
3 various other factors as constraint equations to reach an optimal keyboard
layout that is ergonomically improved.
This task is carried out using a genetic algorithm based optimisation
framework. The new keyboard layout shows better ergonomic performance
than the present keyboard being used in Arabic world with a 36.3%
improvement.
Keywords: keyboard design; genetic algorithm; ergonomic design; global
optimisation.
Reference to this paper should be made as follows: Khorshid, E., Alfadli, A.
and Majeed, M. (xxxx) ‘A new optimal Arabic keyboard layout using genetic
algorithm’, Int. J. Design Engineering, Vol. X, No. Y, pp.000–000.
Biographical notes: Emad Khorshid received his PhD in Mechanical
Engineering at the University of Wisconsin-Madison. Since 1998, he has been
a member of the Mechanical Engineering Department at Kuwait University.
His main research interests are design and evaluation of traffic calming devices,
optimal design of mechanical systems, complex system analysis and whole
body vibration injuries. He has published more than 23 articles in archival
journals and conference proceedings.
AbdulAziz Alfadli received his Master in Mechanical Engineering at Kuwait
University. Since 2001, he is working as Teaching Assistant at the Mechanical
Engineering Department at Kuwait University. His main research interest are
rotor dynamics, systems design optimisation, and vibration.
Majed Majeed received his PhD in Mechanical Engineering at the Virginia
Polytechnic Institute and State University. Since 2005, he has been a member
of the Mechanical Engineering Department at Kuwait University. His main
research interests are structural mechanics, mechanics of composite materials,
vibrations and control. He has published more than 12 articles in archival
journals and conference proceedings.
2 E. Khorshid et al.
1 Introduction
The distribution of the various characters on a keyboard has profound impact on the rate
of typing and typing performance. If frequently used characters are not easily accessible,
the rate of typing will dramatically drop down. An ill-designed keyboard
disproportionately places high load on the weaker fingers leading to typing fatigue, which
possibly causes musculoskeletal injuries. To avoid such injuries and enhance typing rate,
the optimal characters arrangement are sought. For English, European, and Hindi
keyboards, a considerable amount of research was invested towards finding the best
possible arrangement of characters (Pollatschek et al., 1975; Anil et al., 2000; Wagner et
al., 2003). On the other hand, to the knowledge of the authors, there is no firm proof of
whether currently used standard Arabic keyboard is truly optimal and what optimisation
methods have been used. For this reason and the fact that there are other possible
ergonomically optimised layouts has motivated the authors to further investigate an
alternative optimal Arabic keyboard layout based on ergonomic standards.
The optimisation of English keyboard layout based on the typing speed and rapid
learning of the typing system were conducted by Norman and Fisher (1982). Pollatschek
et al. (1975), and Burkard and Offermann (1977) investigated other English layouts using
a broader criterion but did not rely on a realistic model that would have addressed the
problem completely. In general, many optimisation methods were used in keyboard
layout optimisation including, but not limited to, genetic algorithm (GA) (Anil et al.,
2000), simulated annealing (Light and Anderson, 1993), and ant colony optimisation
(Wagner et al., 2003).
Besides the work of Idlebi and Mrayati (1990), who attempted to design a more
efficient Arabic keyboard based on statistical approach no other scientific work was
found and been devoted to obtain an optimal keyboard arrangement for Arabic language
that takes into consideration ergonomic criteria. It is believed that current Arabic
language keyboard layout has been designed by simply adopting English-based computer
systems for Arabic language users. This, to some extent, is not satisfactory because, by
nature, the Arabic language is much different than English. For example, Arabic is
written from right to left and the frequency of letter appearance is different.
The main objective of the proposed study is to optimise the keyboard layout using
GA combined with performance criteria based on six ergonomic factors. The six criteria
used to optimise the Arabic keyboard, given by Yannou and Hossenlopp (2000), are
detailed later in the paper. For comparison purposes, the proposed Arabic keyboard is
compared to the current standard Arabic Keyboard.
2 Optimisation problem formulation
The process of typing any given text can be decomposed into a sequence of key strikes,
namely, a sequence of single or consequent characters usually known as monographs or
digraphs. A monograph is an isolated key strike carried out in the process of typing a text
while a digraph is a sequence of two consecutive keys strikes. Most of previously
published work on keyboard design used criteria based on the distinction between
monograph and digraphs as discussed by Anil et al. (2000) and Wagner et al. (2003).
Based on this distinction, it may be therefore useful to establish statistical data for the
appearance frequency of monographs and digraphs in Arabic literature that would serve
A new optimal Arabic keyboard layout using genetic algorithm 3
as a good ground for optimisation. For this purpose, let fmi and fdi present the statistical
appearance frequencies of monograph mi and digraph di, respectively. The source of these
monograph and diagraph data can be obtained from the available electronic literature
with a wide variety of topics and subjects. The literature sources could vary from
newspapers, electronic books, scientific journals, etc.
Wagner et al. (2001), used Le Monde, Der Spiegel, and USA Today newspapers for
French, German, and English text sources, respectively. On the other hand, Deshwal and
Deb (2004) used various text sources from the internet (15 MB of text ISCII format) for
an Indian keyboard design. Mrayati (1990) showed marginal difference in both
monographs and digraphs frequencies for texts resources taken from three different Arab
countries. Some statistical studies did use the holy Quran as a source to calculate the
frequency of the monographs and the digraphs (Mousa, 1983), which is adopted in the
current work. The reason behind this selection is that, as believed by all Muslims, the
holy Quran is the miracle that challenged the Arabs in their language; nevertheless,
electronic versions sassily accessible. Muslim scholars have considered the linguistic
miracle of the holy Quran as perhaps the most important miraculous aspect of the holy
Quran and studies by Draz, (2001) and Mahmoud, (2004) show that indeed the holy
Quran is a true linguist challenge and it is definitely the one that had the most influence at
the time of the Prophet Muhammad, may the mercy and blessings of God be upon him
(Zarabozo, 2007).
Figure 1 Monograph frequency for Arabic letters in the Quran (see online version for colours)
A computer program was developed for counting the monograph and diagraph from a
digital Quran script and the results are shown in Figure 1 and Figure 2. These figures
demonstrate the statistical frequent appearance of the monograph and digraph
respectively for the main 34 Arabic letters. The y-axis in both figures represents the
monograph and the diagraph scaled to the total number of letters. As can be seen from
4 E. Khorshid et al.
both figures, and as expected, letter ‘ا’ has the highest frequency among all letters. This
result reflects the fact that letter ‘ا’ is the most used letter in the Arabic language.
Furthermore, it is observed from Figure 2 that the digraph ‘ﻞا’ has the highest digraph
frequency among all digraphs. Again, this result is expected because this particular
digraph is the definition article in the Arabic language likewise the article ‘The’ in the
English language.
Figure 2 Diagraph frequency for Arabic letters in the Quran (see online version for colours)
2.1 Representation of the keyboard problem
A keyboard is designed to translate handwritten/typewritten strings of letters into
electronic format using a set of character keys distributed over a board. Analogous to
typewriter, the keys are distributed in a way that can be described by rows and columns
with variety of rules and arrangements. Figure 3 shows the layout of the standard Arabic
keyboard in use. For the problem to be addressed properly, the keyboard is geometrically
represented by rows and columns of ergonomically spaced character-keys and each
character is described by four indices, namely (x1, x2, x3, x4), with the following rules
applied:
A left hand is represented by one and a right hand is represented by zero.
Column indices vary from zero to seven for the left hand and from zero to eight for
the right hand.
Row indices vary from zero to five, zero represents the top row, which will not
contain any letter while five represents the bottom row, which contains keys for
(Space, Alt, and Ctrl). On the other hand, row 3 represents the reference row of
which the typist rests at.
A new optimal Arabic keyboard layout using genetic algorithm 5
Figure 3 Standard Arabic keyboard (see online version for colours)
The indices x1, x2, x3, and x4 are the shift key, side, column number, and row number.
Using the above mentioned rules, any character of the keyboard in Figure 3 can be
mapped to a unique position using the designation (x1, x2, x3, x4). For example and as can
be seen in Figure 4, the letter ‘’ is mapped as (0, 0, 2, 3). Here the first index, zero,
denotes the use of the shift key which is turned off in this case. The second index
represents the right side location (right hand is used for the letter ‘’). The third and the
forth indices, two and three, in this case, represent the second column and the third row,
respectively. Note that the number 18 shown on the top of letter ‘’ in Figure 4 indicates
the mapped relation between the location 18 and the (0, 0, 2, 3).
Figure 4 Standard Arabic keyboard with indices of rows, columns and the finger (see online
version for colours)
Figure 4 shows the mapped position of the standard Arabic keyboard, where the Arabic
keys that contain the Arabic letters/characters (from to ئ) are given numbers from (one
to 34); starting with number 1 for character () and ending with number 34 for character
(ئ). Note that the key of letter (ذ) is not located in series with the other letters therefore it
is numbered as it belongs to the second row in the keyboard. During the optimisation,
each letter/character is initially given an index number equal to the index location of the
key. The location index vector is fixed while the Arabic characters index vector changes
according to the suggested position. Each character (element) in the index vector
representing the Arabic characters is mapped to 4D array (x1, x2, x3, x4) that changes
during the optimisation process. During each iteration step, the index vector representing
the Arabic characters is compared to the index vector representing the
remove and
add ,
6 E. Khorshid et al.
key-location and each letter is relocated to a new position producing a new keyboard,
which is then evaluated by the ergonomic criteria. For example, as will be shown later,
the initial location of the letter () before optimisation is equal to one and the final
location after applying the ergonomic constraints is changed to 28.
Since the objective here is to rearrange the Arabic letters in the standard keyboard in
an optimal layout for better typing performance and efficiency, the skeleton of the
standard Arabic keyboard in terms of the number of rows, number of columns, left hand,
and right hand fingers will not change. Once the standard Arabic keyboard is translated to
the abstraction (array) keyboard of Figure 4, the objective function can then be applied
easily and each letter can be mapped to a specific key location on the board. To keep
track of letter positions, the keys are numbered starting from the right side of the
keyboard sweeping to the left.
2.2 Objective function
In the current study, the objective function, known also as the cost function, represents
the quality of the keyboard layout in the sense that it should:
1 permit for minimum typing effort
2 maximise typing speed
3 reduced typing errors
4 ease of learning and memorising character locations.
In this work, it is proposed that each keyboard arrangement is evaluated based on the six
criteria listed by Yannou and Hossenlopp (2000), namely, load location, number of
keystrokes, hand alternation, consecutive usage of the same finger, avoid large steps, and
hit direction. The final score of the keyboard is determined as a weighted linear
combination of theses six individual criteria. The following sections explain each one of
these six criteria and their mathematical representation.
2.2.1 Load distribution
It is obvious that while typing, the total load on the fingers is constant and since each
finger of the hand has definite strength, some keys are less accessible than others. It
would be highly desirable if this total load can be distributed among the fingers in
proportion of their relative strength.
Mathematically, an ideal load distribution can be assigned between all the
monographs while the performance of any keyboard can be evaluated by determining the
deviation between the actual load distribution for this keyboard and the ideal load
distribution (Eggers et al., 2001). Initially, a load weight factor (wL) can be assigned to
each hand representing a possible difference in their performance and endurance. To
divide the total load equally between the left and right hands this value is chosen to be
50% for each hand. Therefore, each column receives a ratio which represents the relative
agility and endurance of the fingers. Also, each row receives a ratio representing the
relative accessibility. These ratios are presented on Table 1 which is derived from the
work by Eggers et al. (2001). The data in Table 1 are related to ergonomic load on the
typist fingers regarding of the letter (Arabic or English) assigned to the specified key.
criteria
remove
on the board
A new optimal Arabic keyboard layout using genetic algorithm 7
Note that row number 0 in Table 1 is not used for the current layout design since it
represents the function keys (F1 to F12) of the standard keyboard (see Figure 3). The
same assumption is used for the raw number 1 since it represents the numeric numbers
which will not be altered for the new proposed layout design. Note that in Figure 5 the
maximum ideal load distribution max 0.050176
()
=
opt
m
f is given for a monograph that had been
positioned in the third row and the fourth column. Also, since the ideal load distributions
are too small, they were normalised to a maximum of 600.
Table 1 Ideal load distribution for the typist fingers (Eggers et al. 2001)
No. Row (%) Column (%) Finger type
0 10.87 15.38 Little
1 13.04 10.26 Little
2 15.22 15.38 Little
3 43.48 23.08 Little
4 10.87 17.95 Ring
5 6.52 6.41 Middle
6 - 5.13 Index
7 - 3.85 Index
8 - 2.56 Thumb
Figure 5 Ideal load distribution for the left hand (see online version for colours)
Once these ratios of Table 1 are obtained, the optimal load distribution i
opt
m
f
for a
monograph, m = (hand, row, column), can be calculated by multiplying the respective
ratios in Table 1 for a given row and column and then multiplying the result by the ratio
given for the hand being used for typing this monograph. For example, to calculate the
optimal load distribution for the letter ‘أ’ in the standard Arabic keyboard which has a
position of (1, 1, 3) in the indices format. Hence, the first index indicates that the type of
8 E. Khorshid et al.
the hand used to print this letter (zero is given for the right hand while one is given to the
left hand). The remaining two indices indicate that the letter ‘أ’ is positioned in the first
column (little finger) and the third row. Therefore, from Table 1, the optimal load
distribution i
opt
m
f
is calculated as
(weighted row) * (weighted column)
i
opt
L
m
fw=
أ0.5 *(15.38 /100) * (43.48 / 100) 0.033436.
opt
f==
This criterion used to calculate the variance of the load distribution on a keyboard from
the ideal load distribution given by Table 1. Therefore, the score of load distribution is
given by the following equation
(
)
1
2
1ii
m
i
opt
mm
m
vff
∈Ξ
=−
(1)
where 1
m
m
m
f∈Ξ is the set of all monographs represented in Figure 1.
2.2.2 Key number
In order to have an efficient keyboard, the number of keystrokes needed to produce a
given text has to be minimised. Therefore, the score v2 of this criterion is presented by the
ratio of the total number of characters in a given text to the total number of keystrokes
necessary to produce the text. Generally, for different solutions of the KAP (KAP refers
to keyboard arrangement problem as defined in many literatures in the field), this score
has no difference. Hence, the numbers of keys necessary for each character are fixed. In
another word, since the shift key is not used, the number of keys to perform typing is
fixed.
2.2.3 Hand alternation
Typing process will be efficiently fast and more comfortable provided that subsequent
keys are hit by opposite hands. Having this granted will ensure that while one hand is in
the process of typing the first key the other hand has moved to the next key position.
Therefore, the performance of this score is calculated by summing up the frequency of
digraphs which are typed using one hand only. Since this score represents the summation
of all digraph that are typed using the same hand and this summation must be minimised,
In another word any digraph is produced as a combination of two consecutive characters
when these two characters are located in a keyboard such that they are hit by using
fingers of the same hand. This will reduce the typing process. For example the digraph
ﻞأ’ in the keyboard given by Figure 3 is produced by using fingers from both hands and
so that this digraph does not belong to this category. On the other hand the digraph ‘ﻢأ’ is
produced by using fingers of one hand (the right hand). Mathematically, it is easy to
capture this set by using the mapping method (hand, row, column) so when the first index
for the two characters is the same this indicates that they typed by the same hand. This
rule is called the hand alternation rule (HAR) which states that consecutive keys must not
hit by the same hand. Mathematically, this performance is formulated as follows:
A new optimal Arabic keyboard layout using genetic algorithm 9
3
3i
d
i
d
d
vf
∈Ξ
= (2)
where 3
d
i
dΞ is the set of all digraphs that are typed using one hand only and i
d
f
is the
frequency of the HAR of each digraphs.
2.2.4 Consecutive usage of the same finger
Typing process might be slowed down if two consecutive keys are hit by the same finger.
Therefore, in this criterion the above HAR is also applied for the fingers. The score of
this criterion is calculated by summing up the frequencies of all digraphs that are typed
using the same finger and multiplying each of them with distance function. The greater
distance between the two keys of a digraph, the more inconvenience for a consecutive
usage. Mathematically, this index is formulated as:
4
4()
i
d
i
di
d
vfdistd
∈Ξ
= (3)
where 4
d
Ξ is the set of all digraphs that are typed by using the same finger of one hand
and dist (di) is the Manhattan distance function which is given by:
21 12
()| || |
i
dist d c c r r
−+ (4)
where c1 and c2 are the respective columns of the two consecutive keys needed to produce
the diagraph and r1 and r2 are the corresponding rows.
For example, suppose that the diagraph ‘ﻢآ’ is hit by the same finger, then
dist (d) = abs(5–6) + (3–3) = 1 and fdi = 59.74250e-6 (from diagraph data), thus,
v4 = 59.74250e-6.
2.2.5 Avoid big steps
When two consecutive keys hits by the same hand, great distances which need
uncomfortable hand postures should be avoided. This exclusively occurs for keys whose
vertical distance is grater than or equal to one. Therefore, relevant set 5
d
Ξ
is the set of
digraphs which are typed using the same hand, but not the same finger and the vertical
distance between the two keys is greater than or equal to one row. Mathematically, this
index is formulated as following
5
5(, ) i
d
i
d
d
vkijf
∈Ξ
= (5)
where k(i, j) is the weight coefficient of the i and j, first and second finger, respectively.
The values of these coefficients were taken from Wagner et al. (2001) and are presented
in Table 2.
It is clear from Table 2 that the coefficient increases with the following pairs of
fingers (mid-index) has a value of five, (little-index) has a value of six, (little-mid) has a
value of seven, (index-ring) has a value of eight, (mid-ring) has a value of nine, and
(ring-little) has a value of ten.
10 E. Khorshid et al.
Table 2 The values of weight coefficients used for calculating v5
Fin
g
e
r
Thumb Index
M
id
R
in
g
L
ittle
Thumb 0 0 0 0 0
Index 0 0 5 8 6
Mid 0 5 0 9 7
Ring 0 8 9 0 10
Little 0 6 7 10 0
2.2.6 Hit direction
Ergonomic researches have shown that the preferred hit direction for digraphs that are
typed using one hand only is from the little finger towards the thumb (Anil et al., 2000
and Eggers et al., 2001). Since this is the natural finger movement for most people. The
relevant set 6
d
Ξ is therefore the set of digraphs which are typed using one hand and
whose hit direction is not the preferred one. For example, the diagraph ‘نﻤ’ in the
standard keyboard (Figure 4) is typed by one hand and used two different fingers firstly
the ring finger and secondly the mid finger. Therefore, the natural finger movement of
this diagraph does not belong to this category. On the other hand the diagraph ‘حﻤ’ is
typed by one hand and used two different fingers firstly the ring finger and secondly the
little finger and this direction of typing is not the natural movement of fingers, so that this
diagraph is belong to this category. This index can be represented mathematically as
follows:
6
6i
d
i
d
d
vf
∈Ξ
= (6)
2.2.7 Overall score
Once the above partial scores vj (1 j 6) j are obtained, the overall score, which is used
to compare different keyboard arrangements, can be calculated by obtaining a weighted
sum of these scores. Since these scores have different relative importance, limits, and
units, they are first turned dimensionless by dividing each one of these scores by the
respective score of a reference keyboard vj,ref. (the current keyboard) and then multiplying
by a relative weight coefficient γj and summed. The values of the weight coefficients γj
are given in Table 3. They were obtained with the help of two specialised agronomists,
using a pair wise comparison method (Refer to Limayen and Yannou, 2002).
Table 3 Relative weight coefficients γj
Index Relative weight (γj) Score of vjref (standard)
Load distribution (v1) 0.45 0.0341
Key number (v2) 0.5 1.0000
Hand alternation (v3) 1 0.4980
Consecutive usage of the same finger (v4) 0.8 0.1401
Avoid big steps (v5) 0.7 1.4516
Hit direction (v6) 0.6 0.1632
Overall score (vref) - 4.0500
v5:
v is italic font
5 is suberscript
A new optimal Arabic keyboard layout using genetic algorithm 11
Finally, the overall score is formulated as follows:
6
,.
1
j
j
jref
j
v
Vv
γ
=
= (7)
Since the objective function of the keyboard arrangement which is given in equation (7)
needs first to obtain the values of the partial scores of the reference keyboard, and since
the reference is assumed to be the standard Arabic keyboard shown in Figure 3, as
mentioned above, the above six ergonomic criteria can be used to calculate the partial
scores and the objective function to be used in the optimisation process.
The optimisation problem can be presented in a standard optimal design problem
form as follows:
: Arabic characters of 34 letters with their monograph and
diagraph frequencies, the standardArabic keyboard.
: ; 1: 34 where represents the index position of the
letter in the keyboard.
ii
Given
Find x i x
i
T
=
: ( ) where is given in equation (7)
: is integer and 1 34
ii
o minimise f x V V
Subject to x x
=
≤≤
(8)
3 Optimisation method
The optimisation problem statement in equation (8) falls under the integer nonlinear
programming, which can be solved using GA method. The GA method is selected as a
solution method because of its robustness and ability to capture the global optimal
solution for integer variables. The GA toolbox in MATLAB is used as the solver engine
(Math Works, 2006), which can handle only continuous variables. Therefore, the first
step in the solution is to modify the GA optimisation code to deal with integer variables.
The modification was done on the mutation, creation, and population functions. The
original codes for the Creation function are used to generate individual populations. The
original codes for the mutation function are used to generate children. In both creation
and mutation steps the function ‘rand’ is used to generate random continuous
individuals’ population and children, respectively. Thus, to generate random integer
individual’s population and children the rand function is changed to randint function.
With this modification the handling of integer variable is possible with the GA toolbox.
All computations were conducted on Intel® Pentium®4 machine with 1 gigabyte memory
of 333 MHz speed.
4 Discussion and analysis of the results
In this section, several computational simulations are conducted in an attempt to find the
best optimal solution (global optimal). The effect of several parameters in the GA method
is studied towards finding global optimal solution. Also, the new layouts of the Arabic
language keyboard are compared to the currently available standard Arabic keyboard.
space between standard
and Arabic
Small c letter
12 E. Khorshid et al.
The comparison is based on the six proposed ergonomic criteria of equations (1) through
(6).
Figure 6 Evaluation of the optimal solution for 200 generation and 500 populations (see online
version for colours)
The parameters used for the GA toolbox for Case-1 set as follows: the population size is
500, the number of generations 200, the mutation factor is 0.2, and the crossover factor is
0.7. A sample run shows the relation between the objective function and the number of
iteration is presented in Figure 6. Note that in the top portion of the figure, the y-axis
represents the Fitness value, or cost function of equation (7), of the best solution (and the
average fitness values of all solutions in the population) during each iteration. The lower
plot in Figure 5 represent the optimal solution in each iteration during the solution
process until the final best solution which shown in the figure. As a common trend in the
GA method, Figure 6 shows that the solution starts to converge very fast with the initial
generations and then it gets slower until reaching the final optimal solution of 2.6323.
The CPU time for this optimal solution is 5.32 hours. Figure 7 illustrates the effect of
changing the number of population from 500 to 2,000 and the number of generation is
changed from 200 to 220. The final optimal solution is 2.5162 is reached in 20.2 hours of
the CPU time. This run is defined as Case-2. The final results for the best known optimal
solution for Case-2 are shown in Figure 8.
A new optimal Arabic keyboard layout using genetic algorithm 13
Figure 7 Evaluation of the optimal solution for 220 generations and 2,000 population
(see online version for colours)
Figure 8 The optimised arrangement for Arabic language keyboard (see online version for
colours)
Table 4 shows a comparison between the old keyboard and the newly optimised one. It is
clear that each subjective criterion is improved for the new design. The only exception is
that for the partial score (v2), which is related to the key number, because the shift keys
used with Tashkeel is not used in the present work. This can be added in a future work.
Finally, the greatest improvement in the six criteria is the consecutive usage of the same
finger (v4) as shown in Table 4.
14 E. Khorshid et al.
Table 4 Comparison between the optimal arrangement an the keyboards (equal weights for
both hands)
Index Old arrangement
New arrangement
(weighted) % improvement
Load distribution (v1) 1 0.7977 20.23%
Key number (v2) 1 1.0000 0%
Hand alternation (v3) 1 0.7072 29.27%
Consecutive usage of the
same finger (v4)
1 0.3812 61.88%
Avoid big steps (v5) 1 0.5722 42.78%
Hit direction (v6) 1 0.4988 50.12%
Overall score (vref) 1 0.6370 36.30%
5 Effect of changing the total hand load on the optimal solution
In the previous discussion, the total load was divided equally between the two hands. In
this section, the effect of changing the load distribution on the optimisation process and
the resulting layout is investigated. Suppose that a higher load is given for one hand and
consequently, a lower load for the other hand (e.g., a load factor of 0.7 is given for right
hand while 0.3 is given to the left hand). The results of the new layout are shown in
Figure 9 and Table 5, respectively. It was found that the difference between the current
results and the results from the previous section was only in the load distribution score,
which is obvious. As a result, the load distribution is increased from 0.8 for equally
distributed load to 0.85 for unequally distributed load. This increase has resulted in
overloading the right hand. This shows that the new Arabic keyboard layout is insensitive
to the hand used. Therefore, left-handed users or right-handed users will both experience
the same efficiency with the proposed new layout.
Figure 9 The optimised arrangement for Arabic language for right hand 0.7 and 0.3 for the left
hand (see online version for colours)
A new optimal Arabic keyboard layout using genetic algorithm 15
Table 5 Comparison between standard and optimal keyboards with 0.7 for right hand and 0.3
for left hand load
Index Old arrangement
New arrangement
(weighted) % improvement
Load distribution (v1) 1 0.8491 15.0937
Key number (v2) 1 1.0000 0
Hand alternation (v3) 1 0.7073 29.2747
Consecutive usage of the
same finger (v4)
1 0.3810 61.9048
Avoid big steps (v5) 1 0.5722 42.7821
Hit direction (v6) 1 0.4990 50.1048
Overall Score (vref) 1 0.6395 36.05
6 Conclusions
The possibility of optimising the current standard Arabic keyboard based on ergonomic
criteria for best typing performance and for more convenient and effective typing was
discussed; and a new keyboard layout was proposed. The design methodology was based
on six ergonomic criteria using a GA technique. The use of these six ergonomic criteria
permits to evaluate and compare different keyboard layouts for optimal characters
distribution. The mathematical model used for optimisation was based on:
1 best distribution of the typing effort among the ten fingers
2 accessibility of commonly used keys
3 various other factors as constraint equations to reach an optimal keyboard layout that
is ergonomically improved.
The new keyboard layout shows better performance than the present keyboard being
currently used worldwide. The total improvement based on the ergonomic criteria is
36.3% compared to the current keyboard. It was observed that changing the hand load
distribution has a minor effect on the optimised layout design. The overall effect of
changing from equal to unequal hand load distribution was less than 3% from the overall
score. It is suggested to test the new layout on newly trained typists.
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... Khorshid et al. [7], designed an optimized Arabic keyboard layout using a GA. They started by calculating the frequency of each character and pairs of characters, based on the Qur'an holy text. ...
... Genetic algorithms have been successfully used in solving the QAP. In addition, a GA was applied to solve the m-finger Persian keyboard in [12], the m-finger Hindi keyboard in [13], and the m-finger Arabic keyboard in [2] [7]. As far as we know, it has not been previously used in the optimization of s-finger keyboards in any language. ...
... As mentioned previously, the optimized keyboard produced by [7], and [2] are meant for many-finger keyboards; so we can notice that the pair ( ) are placed separately from each other. This is favorable for m-finger keyboards, because each letter will be typed with different fingers in different hands. ...
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Zusammenfassung Zur Ermittlung möglichst günstiger Schreibmaschinentastaturen wird ein quadratisches Zuordnungsproblem aufgestellt und suboptimal gelöst. Die dazu notwendigen sprachstatistischen Daten (Häufigkeit von Buchstaben und Buchstabenpaaren in verschiedenen Sprachen) wurden durch Auszählung von Texten von über 100.000 Buchstaben gewonnen. Es werden sowohl verbesserte Tastaturen für deutsche, englische, französische und niederländische Texte wie auch für gemischt-sprachliche Texte angegeben. Mit den vorgeschlagenen Methoden wurden Verbesserungen der Schreibgeschwindigkeit in der Größenordnung von 7%–10% gegenüber der derzeitigen Standardtastatur erzielt.
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A search of the psychological, technical, and promotional literature was conducted to compile information relevant to key, keyboard, and operator characteristics. The most recent and significant articles were discussed and evaluated. Where possible, general conclusions have been drawn to aid the keyboard designer.
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In order to solve the problem of the arrangement of letters on a computer keyboard, an abstract representation of a keyboard is introduced and an evaluation function taking account of ergonomic criteria is proposed. It results in a new optimization problem that we name the keyboard arrangement problem. Based on the generic framework of Ant Colony optimization, an algorithm is developed and applied to this problem. New effective keyboard arrangements are deduced for several languages. Comparisons are made with standard manually optimized keyboards.
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In this paper, we present a new design of the Hindi keyboard for convenient typing. We describe the ergonomic criterion we have used to evaluate and compare keyboards. This criterion is a mathematical formulation of keyboard optimality in terms of the distribution of the typing effort among the ten fingers, accessibility of commonly used keys and various other factors. Measured against this criterion, our keyboard performs more than twice as better than the standard Hindi keyboard. We also describe a genetic algorithm based optimization framework which we use to arrive at our new keyboard design. Finally, we perform some sensitivity analysis on our optimization procedure and demonstrate that our results confirm to intuitive expectations.
Optimizing ambiguous keyboard layout for the English language via genetic algorithm', Final project in Computational Intelligence CSCI
  • B Anil
  • C Ramakrishnan
  • D Ashish
  • D Crouch
  • S Hasan
Anil, B., Ramakrishnan, C., Ashish, D., Crouch, D. and Hasan, S. (2000) 'Optimizing ambiguous keyboard layout for the English language via genetic algorithm', Final project in Computational Intelligence CSCI 8940, Spring semester 2000, Intelligence Center, University of Georgia, Athens.