Genetic algorithms are often used for
optimization problems due to its ability in finding solutions
that are global optima in a problem. The performance of the
genetic algorithm is determined by the diversity within a
population. One of the factors that determine the diversity in
the population is the crossover process. In the process of
crossover, the value of Alpha as a multiplying factor will
determine the diversity of the population is the result of the
crossover. Crossover method used in this research is the
method of arithmetic crossover and the problems that used in
this study is Traveling Salesman Problem (TSP). The purpose
of this study was to obtain an overview of the influence of the
alpha value in the arithmetic crossover to the performance of
the genetic algorithm.
Published In : IJCAT Journal Volume 2, Issue 7
Date of Publication : July 2015
Pages : 240 - 246
Figures :04
Tables : 07
Publication Link :The Influence of Alpha Value as Multiplier Factor
on Arithmetic Crossover in Genetic Algorithm
Hartono : received the Master degree in
2010 from the University of Putra Indonesia
“YPTK” Padang, Indonesia in Computer
Science and Bachelor Degree in 2008 from
STMIK IBBI Medan, Indonesia in Computer
Science. He is a lecturer at STMIK IBBI
Medan. His current interests are in data
mining and artificial intelligence. Nowadays,
He Is a Student in a Doctoral Program in
Computer Science at University of
Sumatera Utara
Erianto Ongko : received the Master degree
in 2015 from the University of Sumatera
Utara, Indonesia in Computer Science and
Bachelor Degree in 2012 from STMIK IBBI
Medan, Indonesia in Computer Science. he
is a designer and also Copilot at Top Coder
Studio. His current interests are in design,
data mining, and artificial intelligence.
Genetic Algorithm
Alpha Value
Arithmetic
Crossover
The conclusion that can be drawn from this study are as
follows.
1. The increasing in the value of alpha, which is a
multiplier factor on the arithmetic crossover can improve diversity in a population that is characterized
by an increase in the performance of the genetic
algorithm.
2. Alpha value of 0.9 is the alpha value which gives the
highest performance results both for the whole
arithmetic crossover method, simple arithmetic
crossover, and a single arithmetic crossover.
[1] Holland JH. 1975. Adaptation in Natural and Artifcial
Systems. The University of Michigan Press: Ann
Arbor.
[2] Goldberg, David E. 1989. Genetic Algorithms. Pearson
Education: London
[3] Sivanandam SN, Deepa SN. 2007. Introduction to
Genetic Algorithms. Springer-Verlag: Berlin.
[4] Picek, Stjepan, Jakobovic, Domagoj and Gloub, Marin.
2013. On the Recombination Operator in The Real-
Code Genetic Algorithms, 2013 IEEE Congress on
Evolutionary Computation, pp. 3103-3110
[5] Russell, Stuart And Norvig, Peter. 2009. Artificial
Intelligence: A Modern Approach. 3rd Edition. Pearson
Education Limited: London
[6] Pasquier, Thomas, and Erdogan, Julien. 2010, Genetic
Algorithm Optimization. Institut Superieur
d'Electronique de Paris
[7] Lozano, Manuel, Herrera, Francisco and Cano, Jose
Ramon. 2008, Replacement Strategies to Preserver
Useful Diversity in Steady-State Genetic Algorithm.
Information Sciences178: 4421-4433
[8] Konar, Amit. 2005. Computational Intelligence
Principles, Techniques, and Applications. Springer:
Calcutta, India
[9] Eiben, A.E. & Smith, J.E. 2007. Introduction to
Evolutionary Computing Genetic Algorithms. Springer:
New York
[10] Ongko, Erianto. 2015. Performance Analysis of the
Method Arithmetic Crossover in Genetic Algorihtm.
Thesis. University of Sumatera Utara
[11] Simões, A, Costa, E. 2001. Using Biological
Inspiration to Deal with Dynamic Environments.
Proceedings of the Seventh International Conference
on Soft Computing (MENDEL’01), pp. 7-12, Brno,
Czech Republic, 6-8 June, Brno University of
Technology, 2001
[12] Deb, Kalyanmoy and Agrawal, Samir. 1999.
Foundations of Genetic Algorithms. Morgan
Kaufmann: San Mateo
[13] Rabunal, Juan R. and Dorado, Julian. 2006. Artificial
Neural Networks in Real-Life Applications, Ideal
Group Publishing: Hershey, United States of America.
[14] Mitchel, Mielanie. 1999. An Introduction to Genetic
Algorithm. MIT Press: Massachusets
[15] Scrucca, Luca. 2013. GA: A Package for Genetic
Algorithm in R. Journal of Statistical Software53(4):
1-37
[16] Kumar, Rakesh and Jyotishree. 2012. Blending
Roulette Wheel Selection & Rank Selection in Genetic Algorithms, International Journal of Machine
Learning and Computing2(4): 365-370
[17] Hassoun, Mohammad H. 1995. Fundamentals of
Artificial Neural Networks. MITPress: Massachusetts
[18] Kaelo P. and Ali M.M. 2007. Integrated crossover rules
in real coded genetic algorithms. European Journal of
Operational Research176 (1): 60-76.
[19] Michalewicz Z. 1998. Genetic algorithms + data
structures = evolution programs. Springer-Verlag:
New York
[20] Herrera F., Lozano M. and Verdegay J.L. 1998.
Tackling real coded genetic algorithms: operators and
tools for behavioural analysis. Artificial Intelligence
Review2: 265-319.
[21] Malhotra, Rahul, Lozano M. and Verdegay J.L. 1998.
Tackling real coded genetic algorithms: operators and
tools for behavioural analysis. Artificial Intelligence
Review2: 265-319.
[22] Biggs, N.L., Lloyd, E.K. and Wilson, R.J. 1976. Graph
Theory 1736-1936. Clarendon Press: Oxford
[23] Lin, Chu Hsing, Yu, Jui Ling, Liu, Jung Chun, Lai,
Wei Shen and Ho, Chia Han. 2009. Genetic Algorithm
For Shortest Driving Time in Intelligent Transportation
System. Internation Journal of Hybrid Information
Technology2(1): 21-30
[24] Samuel, Lukas, Toni, A. dan Willi, Y.. 2005.
Penerapan Algoritma Genetika Untuk Salesman
Problem Dengan Menggunakan Metode Order
Crossover dan Insertion Mutation. Prosiding Seminar
Nasional Aplikasi Teknologi Informasi 2005 (SNATI
2005), pp. I-1 – I-5 \
[25] Annies, Hannawati, Thing, Eleazar. 2002. Pencarian
Rute Optimum menggunakan algoritma genetika.
Jurnal Teknik Elektro Fakultas Teknologi Industri,
Jurusan Teknik Elektro, Universitas Kristen Petra2(2):
78-83
