There are many problems in the real world that can be modeled as large scale global optimization problems. Usually, large scale global optimization problems are global optimization problems where the dimensions are greater than or equal to 1000. In this research, we propose a genetic algorithm that can be used to solve large scale optimization problems with dimensions up to 100000. To measure the capabilities of the proposed genetic algorithm, we use five different test functions. Based on the results obtained, it can be inferred that the proposed genetic algorithm can find a good solution in a fairly short time.

Original languageEnglish
Article number012055
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 29 Mar 2021
Event6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020 - Surabaya, Virtual, Indonesia
Duration: 24 Oct 2020 → …


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