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首页> 外文期刊>International Journal on Informatics Visualization: JOIV >Contrasting of Various Algorithmic Techniques to Solve Knapsack 0-1 Problem
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Contrasting of Various Algorithmic Techniques to Solve Knapsack 0-1 Problem

机译：求解背包0-1问题的各种算法技术的对比

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This paper will point of convergence on a relative assessment and estimation of the dynamic programming, B&B, Greedy and Genetic algorithm including of the intricacy of time prerequisites, and the necessary programming endeavors and inspect the absolute incentive for every one of them. Out of these four, Two algorithm (Greedy and Genetic) algorithm can be utilized to clear up the 0-1 Knapsack issue inside a sensible time multifaceted nature. The most pessimistic scenario time unpredictability (Big-O) of the two calculations is O(N). Parallely, these calculations can't find the accurate response to the issue; they are valuable in detecting a close by premier final product as it were. Our basic commitment directly here is to investigate the two calculations contrary to common benchmark realities units and to quantify the precision of the impacts provided by method for each calculation. In this way, we will think about the top notch neighbourhood result created by utilizing the calculation against the genuine real most dependable outcome.
机译：本文将收敛对动态编程，B＆B，贪婪和遗传算法的相对评估和估计，包括时间前述的复杂性，以及必要的编程努力，并检查每个人的绝对激励。在这四个中，可以利用两种算法（贪婪和遗传和遗传）算法在明智的时间内部多方面的性质内清除0-1个背包问题。两个计算的最悲观的情景时间不可预测性（Big-O）是O（n）。截然，这些计算无法找到对问题的准确响应;它们在检测到首次最终产品的附近是有价值的。我们直接的基本承诺是调查与普通基准现实单位相反的两个计算，并量化每种计算方法提供的影响的精度。通过这种方式，我们将考虑通过利用对真正最可靠的结果的计算来创建的顶级距离邻居结果。

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