The Model of Elementary School Teachers Placement in Magelang District by Using Genetic Algorithm

Sriwindono, Haris; Rosa, Paulina H. Prima; Polina, Agnes Maria; Nugroho, Robertus Adi

doi:10.1145/3168390.3168414

Cited by 1 publication

(2 citation statements)

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“…OX builds offspring by choosing tour sub-sequences from one parent, then taking from the other parent cities that are not in the sub-sequence. Choose a sub-sequence tour by determining the bottom pot and the pot for sub-sequences [13]. The cities taken are placed sequentially in filling empty positions on the chromosome, as shown in Figure 3.…”

Section: Fig 2 Roulette Wheelmentioning

confidence: 99%

“…For example, a mutation occurs after 1000 times of crossover or 1: 1000 against crossover. There are many techniques that can be applied in the genetic algorithm mutation method, one of them is the Reciprocal-exchange technique or by exchanging positions on two genes in the chromosome [13]. Examples of mutations with the Reciprocal-exchange technique are shown in figure 4.…”

Section: Fig 3 Ordered Crossovermentioning

confidence: 99%

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The Comparison of Genetic Algorithm and Ant Colony Optimization in Completing Travelling Salesman Problem

Alexander¹,

Sriwindono²

2020

Proceedings of the 2nd International Conference of Science and Technology for the Internet of Things, ICSTI 2019, September 3rd

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Traveling Salesman Problem abbreviated as TSP, is a NP-hard problem that is often applied in various applications. TSP is a polynomial problem, so the solution is exponential. One way to improve the resolution of NP-hard problems is to use probabilistic algorithms such as genetic algorithms, ant colony optimization algorithms, and others. In this study genetic algorithm (GA) was applied with ordered crossover method and reciprocal mutation method. And also use the ant colony algorithm (ACO). This research will compare the performance of the two algorithms. The data used are 10, 20, ..., 100 cities, so the result shows that the ant colony algorithm is able to find a shorter distance than the genetic algorithm, but the genetic algorithm shows a better speed of completion than the ant colony algorithm.

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Section: Fig 2 Roulette Wheelmentioning

confidence: 99%

Section: Fig 3 Ordered Crossovermentioning

confidence: 99%