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.

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