On the Applicability of Genetic Algorithms in Subset Sum Problem

Oberoi, Apeksha; Gupta, Jyoti

doi:10.5120/ijca2016910765

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…With the developed Hybrid Algorithm, the genetic algorithm is accelerated and the cost value is improved. [19]. In the hybrid algorithm developed within the scope of the study, 1.8 cost value was obtained.…”

Section: Resultsmentioning

confidence: 99%

Yeni Bir Metahuristik Yaklaşımla Alt Küme Toplamı Probleminin Çözümü Ve Performans Analizi

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2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Subset sum problem was solved with two different metaheuristic approaches in the study. After these approaches, which are simulated annealing and genetic algorithms, a hybrid model of two methods was created and better results were obtained. The observed results were compared with other methods in the literature and the best time cost results were yielded owing to the hybrid algorithm developed in the study. The algorithms used gave successful results in terms of Cost values too. Performance analyses were measured on the Subset Sum Problem, defined as NP-Complete problem in computer science, with different functions used in these methods. Thus, the success of the sub-functions of the commonly used Simulated Annealing and Genetic Algorithm methods were compared and the findings were yield that could guide the researchers in other studies.

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Section: Resultsmentioning

confidence: 99%