ABSTRACT Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP

Nailah Al-Madi

Simone A Ludwig

CiteSeerX

Segment-Based Genetic Programming
Nailah Al-Madi and Simone A. Ludwig
Department of Computer Science
North Dakota State University
Fargo, ND 58102, USA
{nailah.almadi,simone.ludwig}@ndsu.edu
ABSTRACT
Genetic Programming (GP) is one of the successful evolu-
tionary computation techniques applied to solve classifica-
tion problems, by searching for the best classification model
applying the fitness evaluation. The fitness evaluation pro-
cess greatly impacts the overall execution time of GP and is
therefore the focus of this research study. This paper pro-
poses a segment-based GP (SegGP) technique that reduces
the execution time of GP by partitioning the dataset into
segments, and using the segments in the fitness evaluation
process. Experiments were done using four datasets and the
results show that SegGP can obtain higher or similar accu-
racy results in shorter execution time compared to standard
GP.
Categories and Subject Descriptors
G.1.6 [Numerical Analysis]: Optimization, Global opti-
mization; I.2.8 [Artificial Intelligence]: Problem Solving,
Control Methods, and Search
General Terms
Optimization, Algorithms, Performance, Experimentation
Keywords
Evolutionary computation, genetic programming, fitness eval-
uation, data classification
1. INTRODUCTION
Evolutionary computation is a problem solving technique
that mimics the mechanism of natural operations and sur-
vival of the fittest. Genetic Programming (GP) [1] is one
of the evolutionary computation approaches that proved its
efficiency in optimization and problem solving such as clas-
sification. GP solves the problems by generating programs
that are constructed from mathematical and logical func-
tions and terminals (variables and constants). The solution
quality provided by each program is evaluated using a fitness
function. The main drawback that prevents the use of GP in
real time problem solving is its long execution time, which
mainly depends on the number of generations, the popula-
tion size, and the fitness evaluation. The fitness evaluation
is the most computational intense process in GP [2].
Copyright is held by the author/owner(s).
GECCO’13 Companion, July 6–10, 2013, Amsterdam, The Netherlands.
ACM 978-1-4503-1964-5/13/07.
When GP is applied to solve a classification task, each pro-
gram presents a classifier model that distinguishes classes in
order to apply this model to new data records. The fitness
function for a classification task is the classifier accuracy
(number of correctly classified records). Therefore, the exe-
cution time is dependent on the dataset size.
This paper proposes a segment-based GP (SegGP) that
accelerates the fitness evaluation process without affecting
the classification accuracy. SegGP is based on the idea of
decreasing the training dataset size, but at the same time
covering the whole training dataset by partitioning the train-
ing dataset into segments.
2. PROPOSED APPROACH
The fitness evaluation step when solving a classification
problem using GP, refers to the program execution and the
calculation of its classification accuracy. Therefore, the num-
ber of fitness evaluations (FE) of an independent GP run is
related to population size (P ), dataset size (D), and number
of generations (G). A single program fitness calculation is
equal to (D) since the program is executed for every record
in the dataset. Thus, the total number of fitness evaluations
for a GP run is FE = D × P ×G.
The proposed SegGP approach accelerates the fitness eval-
uation time during the GP run by reducing the dataset size
(D). It is based on creating segments of the training dataset,
which is done using the resample supervised instance fil-
tering of the Weka Data mining software [3]. Therefore,
whenever GP performs a fitness evaluation for a program it
randomly chooses one of these segments and executes the
program to obtain the fitness. Each program may choose
different segments. The training dataset is partitioned into
ten segments. Size of the segments (S) is a percentage of the
training dataset, where S < D. Using this technique, the
number of fitness evaluations is reduced to FE = S×P ×G.
The whole training dataset is covered by the population.
In addition, if a program survives for multiple generations,
it is likely to cover the whole dataset by using different seg-
ments in each generation, and thus, the program is trained
on the whole dataset. There is a high probability that
this occurs with segments of large percentage of the whole
dataset. It is important to note that the entire testing
dataset is used without any partitioning.
3. EXPERIMENTS AND RESULTS
For the evaluation of SegGP, experiments were performed
using the JGAP [4] on four datasets [5]. The description
of these datasets are shown in Table 1 (reduced number of
Table 1: Datasets
Dataset Classes Features Records
D1 Breast 2 31 (11) 568
D2 Diabetes 2 8 ( 4) 768
D3 Lymph 4 19 (10) 148
D4 Dermatology 6 33 (19) 366
features are shown in the brackets). The GP settings were
as follows: Population size = 500, Number of generations
= 1000, Crossover probability = 0.5, Mutation probability
= 0.1. Moreover, 23 functions and 14 variables/constants
are used. The experiments are applied using 66% of the
dataset for training, and the remaining 34% for testing. In
order to compare our proposed SegGP with standard GP 50
independent runs were performed.
80
85
90
95
10
0
80
85
90
95
10
0
80
85
90
95
10
0
10 20 30 40 50 60 70 80 90 100
Segment Size [%]
A
cc
ur
ac
y 
[%
]
(a) D1 Accuracy
68
70
72
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80
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80
10 20 30 40 50 60 70 80 90 100
Segment Size [%]
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cc
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ac
y 
[%
]
(b) D2 Accuracy
50
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85
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10 20 30 40 50 60 70 80 90 100
Segment Size [%]
A
cc
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ac
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[%
]
(c) D3 Accuracy
40
50
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40
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40
50
60
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80
10 20 30 40 50 60 70 80 90 100
Segment Size [%]
A
cc
ur
ac
y 
[%
]
(d) D4 Accuracy
Figure 1: Accuracy results
To find the best segment size to use, we ran SegGP with
different segments sizes, starting with a segment size of only
10% of the training dataset (“Seg10”), up to using the whole
dataset (100%, “Seg100”) in steps of 10% increments (Seg20,
Seg30, Seg40 ... Seg90, Seg100). The accuracy results for
the four datasets are shown using the box plots in Figure 1.
It can be concluded that Seg90 has higher accuracy values
than Seg100 for D3. Seg80 has three accuracy values higher
than Seg100 for D1, D2, and D4. Moreover, Seg60 has two
values higher than Seg100 for D2 and D4. However, Seg70
has 3 accuracy values close to Seg100 for D2, D3 and D4.
The execution time results averaged over 50 runs are shown
in Figure 2, where all the results confirm that a smaller
segment size runs faster than a larger segment size.
4. CONCLUSION
A segment-based genetic programming (SegGP) approach
is proposed, that accelerates the genetic programming fitness
D1 D2 D3 D4
Datasets
E
xe
cu
tio
n 
T
im
e 
(s
)
0
20
40
60
80 10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Figure 2: Execution time
evaluation process. SegGP is based on the idea of reducing
the training dataset size but at the same time covering the
whole training dataset. SegGP aims to obtain the same
classification accuracy with a shorter execution time than
standard GP. A comparison of standard GP and SegGP was
conducted using ten segments with different percentage sizes
of the whole dataset, and applied on four datasets. In sum-
mary, SegGP obtains the same or in some cases higher accu-
racy than standard GP, using Seg80 or Seg60, and a speedup
up to 24.14% or 39.54%, respectively is achieved.
Future work will involve testing SegGP on larger datasets
and also involve different optimization tasks such as regres-
sion and clustering.
References
[1] A. E. Eiben and J. E. Smith. Introduction to Evolution-
ary Computing. SpringerVerlag, 2003.
[2] R. Poli, W. B. Langdon, and N. F. McPhee.
A field guide to genetic programming. Pub-
lished via http://lulu.com and freely available at
http://www.gp-field-guide.org.uk, 2008.
[3] I. Witten, E. Frank, and M. Hall. Data Mining: Practical
Machine Learning Tools And Techniques, 3rd Edition.
Morgan Kaufmann, 2011.
[4] K. Meffert et al., JGAP - java genetic algorithms
and genetic programming package [online]. available:
http://jgap.sf.net, January 2012.
[5] A. Frank and A. Asuncion. UCI machine learning repos-
itory, available: http://archive.ics.uci.edu/ml, 2010.


Segment-Based Genetic Programming

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