Open AccessComparison between genetic algorithm with Differential Evolution in study scheduling
Author(s) -
N Lukman,
Muhammad Naeem Irfan,
Antony Purnama Nugraha,
J Jumadi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1098/3/032082
Subject(s) - crossover , differential evolution , initialization , computer science , mathematical optimization , meta optimization , algorithm , scheduling (production processes) , genetic algorithm , population based incremental learning , selection (genetic algorithm) , optimization problem , mathematics , artificial intelligence , programming language
This paper proposed to discuss the complexity of scheduling by comparing two optimization methods between genetic algorithms with differential Evolution. Genetic Algorithms can solve the simplest to complex problems as well. Therefore, the Genetic algorithm is precisely applied to the scheduling of subjects. Then another appropriate optimization method for completing optimization is the Differential Evolution (DE) algorithm. DE algorithm is a fast and effective search algorithm in solving numerical and finding optimal global solutions. The steps of the two algorithms are initialization, participation, mutation, crossover, and selection. The scheduling system produces non-optimal schedules for teacher conflicts and empty slot schedules. After the genetic algorithm and differential evolution are applied, an analysis of the results of the subject scheduling is then performed by comparing the fitness values and the execution speed of the two algorithms. the genetic algorithm found only 2 perfect schedules out of 10 experiments, whereas in the implementation of differential algorithms, there are 7 perfect schedules out of 10 experiments. Thus, it can be concluded that by determining the value of the producing parameters 5, generation 50, mutation 0.6, and crossover 0.2, the differential evolution produces better output or conformity values using genetics.