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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12985

Title: Meta-Heuristics Approach to Knapsack Problem in Memory Management
Authors: Oppong, Emmanuel Ofori
Oppong, Stephen Opoku
Asamoah, Dominic
Kordzo Abiew, Nuku Atta
Keywords: Knapsack
memory management
genetic algorithm
simulated annealing
Issue Date: Apr-2019
Publisher: Asian Journal of Research in Computer Science
Citation: Asian Journal of Research in Computer Science,3(2), 1-10
Abstract: The Knapsack Problems are among the simplest integer programs which are NP-hard. Problems in this class are typically concerned with selecting from a set of given items, each with a specified weight and value, a subset of items whose weight sum does not exceed a prescribed capacity and whose value is maximum. The classical 0-1 Knapsack Problem arises when there is one knapsack and one item of each type. This paper considers the application of classical 0-1 knapsack problem with a single constraint to computer memory management. The goal is to achieve higher efficiency with memory management in computer systems. This study focuses on using simulated annealing and genetic algorithm for the solution of knapsack problems in optimizing computer memory. It is shown that Simulated Annealing performs better than the Genetic Algorithm for large number of processes.
Description: This article is published in Asian Journal of Research in Computer Science and also available at DOI: 10.9734/ajrcos/2019/v3i230087
URI: 10.9734/ajrcos/2019/v3i230087
Appears in Collections:College of Science

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