<![CDATA[There are several problems on thesis scheduling in Informatics Engineering Study Program Faculty of Computer and Science Brawijaya University that makes thesis scheduling ineffective. Significant differences in the number of students and lecturers in 2017 as well as thesis trial registration in the adjacent time is often a problem in scheduling thesis hearings. Ineffective scheduling will take a long time. Therefore, need a system that can be made using a distributed genetic algorithm method to do thesis trial scheduling. The first step in system is randomize chromosomes and then the population will be divided into several subpopulations, and will go through the reproductive stage, then through evaluation to calculate the fitness value. Selection process will be selected for the next generation. In the distributed genetic algorithm a migration process will be carried out to increase the diversity of individuals by exchanging individuals from one subpopulation to another. Based on the test, the optimal parameter value in the thesis trial scheduling is 11 populations, 1750 generations, the crossover rate is 0.7 mutation rate 0.3 and sub-populations with an average fitness value of 0.00010232. From the results of the test, if there is more population and generation, the wider and bigger the search area for the solution will be, but a cost of a longer computing time.]]>
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