Murat Erşen BerberlerAsli GulerUrfat NuriyevBerberler, Murat ErsenGuler, AsliNuriyev, Urfat G.2025-10-06201322978747, 1300686X2297-87471300-686X10.3390/mca180304862-s2.0-84884225064https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884225064&doi=10.3390%2Fmca18030486&partnerID=40&md5=fe1a972d53f812d5f9158cea723fc6c6https://gcris.yasar.edu.tr/handle/123456789/10136https://doi.org/10.3390/mca18030486https://search.trdizin.gov.tr/en/yayin/detay/233455In this paper The Multidimensional Knapsack Problem (MKP) which occurs in many different applications is studied and a genetic algorithm to solve the MKP is proposed. Unlike the technique of the classical genetic algorithm initial population is not randomly generated in the proposed algorithm thus the solution space is scanned more efficiently. Moreover the algorithm is written in C programming language and is tested on randomly generated instances. It is seen that the algorithm yields optimal solutions for all instances. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessEvolutionary Algorithms, Genetic Algorithm, Heuristic Approach, Multidimensional Knapsack Problem, C (programming Language), Combinatorial Optimization, Evolutionary Algorithms, Heuristic Algorithms, Heuristic Methods, Heuristic Approach, Initial Population, Multidimensional Knapsack Problems, Optimal Solutions, Solution Space, Genetic AlgorithmsC (programming language), Combinatorial optimization, Evolutionary algorithms, Heuristic algorithms, Heuristic methods, Heuristic approach, Initial population, Multidimensional knapsack problems, Optimal solutions, Solution space, Genetic algorithmsGenetic AlgorithmMatematikBilgisayar Bilimleri, Teori Ve MetotlarEvolutionary AlgorithmsHeuristic ApproachMultidimensional Knapsack ProblemArticle