TY - JOUR AU - Saragih, Rijois Iboy Erwin PY - 2016/09/10 Y2 - 2024/03/29 TI - PENGENDALIAN MUTATION RATE PADA ALGORITMA GENETIKA JF - METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JA - METHODIKA VL - 2 IS - 1 SE - Articles DO - 10.46880/mtk.v2i1.25 UR - https://ejurnal.methodist.ac.id/index.php/methodika/article/view/25 SP - 120-123 AB - <p>Genetic algorithm is heuristic searching algorithm which based on nature selection of mechanism and nature genetic. The <br>basic concept that inspires the genetic algorithm is that evolution theory. In the process of searching the best solution on <br>classic genetic algorithm often occurs optimum local. Optimum local is a common problem which is often occurs in genetic <br>algorithm, and one of the reasons is that because of the population diversity. If population diversity is too low is led to <br>optimum local, and if it is too high caused more times to look for the best solution. Mutation operator plays an important role <br>in the process of genetic algorithm to manage that population diversity and an important element of mutation operator is <br>mutation rate. On classic genetic algorithm that mutation rate is set in the beginning while the process of genetic algorithm <br>depends on how many generations are. Therefore is needed to control mutation rate in generation. Controlling mutation <br>operator, especially mutation rate based on Fuzzy Logic Controller (FLC) to manage population diversity, not too high or too <br>low, in order to get optimal result. Evaluation is done 10 times execution by comparing the performance of standard genetic <br>algorithm (GA), IAG and genetic algorithm based on Fuzzy Logic Controller (FLC) and experimental results show that there <br>is an improvement on genetic algorithm performance based on FLC.</p> ER -