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All Journal International Journal of Quantitative Research and Modeling
Hasanah, Indah Mauludina
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1020

Abstract

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.
Application of Genetic Algorithm on Knapsack Problem for Optimization of Goods Selection Hasanah, Indah Mauludina; Mulyo, Lukman Widoyo; Khan, Muhammad Fardeen; Hidayana, Rizki Apriva
International Journal of Quantitative Research and Modeling Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1020

Abstract

Knapsack Problemis one of the combinatorial optimization problems that often arise in everyday life, especially in making decisions about selecting goods with limited capacity. This study combines two previous studies that apply genetic algorithms to real cases: the selection of basic necessities and packaged fruits in limited containers. Genetic algorithms are used because they are flexible and able to find more than one optimal solution. The process includes the formation of an initial population, fitness evaluation, selection (roulette wheel), crossover, and mutation. From the two case studies analyzed, it was found that genetic algorithms consistently produce increased fitness between generations and are able to maximize the value of goods without exceeding capacity or budget limits. This study strengthens the potential of genetic algorithms as an effective method in solving Knapsack Problems based on real needs.
Co-Authors Hidayana, Rizki Apriva Khan, Muhammad Fardeen Mulyo, Lukman Widoyo