Open AccessComparison of Crossover and Mutation Operators to Solve Teachers Placement Problem by Using Genetic Algorithm
Author(s) -
Paulina H. Prima Rosa,
Haris Sriwindono,
Robertus Adi Nugroho,
Agnes Maria Polina,
Kartono Pinaryanto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1566/1/012021
Subject(s) - crossover , mutation , genetic algorithm , population , mathematics , value (mathematics) , sequence (biology) , chromosome , algorithm , computer science , mathematical optimization , statistics , genetics , artificial intelligence , medicine , biology , environmental health , gene
The placement of elementary school teachers is an NP-complex problem. Teacher placement can be optimized by considering several factors that influence their performance, including the distance of teacher’s residence to school, age, and gender of the teacher. This paper discusses the solution model of the problem based on genetic algorithms by finding a chromosome formation that represents the possibility of teachers placement solution, composing a population, and finding the recommended combination of two selected mutations operators and two selected crossover operators to achieve optimal results. The selected mutation operators were Reverse Sequence Mutation (RSM) and Partial Shuffle Mutation (PSM), while the selected crossover-operators were Single Point Crossover (SPX) and Ordered Crossover (OX). The combined performance of these operators is measured based on the fitness value and running time of the program. Based on experiments, it can be concluded that the combination of OX-PSM with mutation probability 1:20 gives the lowest minimum fitness value compared to other combinations of crossover and mutation operators. The running time of the combination of OX-PSM is stable in any mutation probability, ranging from 39,5 – 41 minutes.