Computational Complexity Analysis of Combinatorial Algorithms in theContext of the Knapsack Model

The Knapsack problem is a complex optimization challenge, often impractical to solve exhaustively. Categorized as a Non-Deterministic Polynomial problem, it allows fast solution verification, but solution discovery time is unpredictable.The aim of this study is to compare the complexities of the knapsack model using greedy, genetic, and dynamic programming algorithms. Our findings, shows that the greedy algorithm outperforms genetic, and dynamicprogramming algorithms in terms of time complexity, providing the most efficient solution for the knapsack model. Future study will delve into hybrid approaches, aiming to leverage theunique strengths of each algorithm and alleviate their limitations