Abstract
Genetic algorithm (GA) is an optimization algorithm that is often categorized as a global search heuristic technique. Being a branch of evolutionary computation, it is known to mimic the natural selection of biological processes of reproduction and to solve the ‘fittest’ solutions. In this chapter, the knapsack problem was solved using GA technique to determine the strength or capacity of bag used in convey items. The solution of the model shows that no combination of any form would give an exact weight or capacity the bag can carry except set spaces 15 and 29, where the weight of items are 34 kg and 36 kg respectively. Hence the feasible weight of item to be stored in the bag is 34 kg at a value of 16. Any weight of material above 36 kg will lead to the ripping of the bag.
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References
Simon, D. 2013. Evolutionary optimization algorithms: Biologically inspired and population-based approaches to computer intelligence. Hoboken: Wiley.
Raymer, M.L., W.F. Punch, E.D. Goodman, L.A. Kuhn, and A.K. Jain. 2000. Dimensionality reduction using genetic algorithms. IEEE Transactions on Evolutionary Computation 4 (2): 164–171.
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Okwu, M.O., Tartibu, L.K. (2021). Genetic Algorithm. In: Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Studies in Computational Intelligence, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-61111-8_13
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DOI: https://doi.org/10.1007/978-3-030-61111-8_13
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