Practical Approach Of Genetic Edge

The greedy algorithm effectively solves the Fractional Knapsack Problem (FKP) in linear scenarios. However, the problem's complexity increases, and the greedy approach fails to yield optimal solutions when additional constraints, such as weight and risk, are introduced. Our previous theoretical study highlighted the limitations of the greedy method in such multi-constraint environments. This paper presents the real-world application of a Genetic algorithm for these challenges. The FKP is not like 0-1 Knapsack problem, it allows fractional item selection offering greater flexibility in practical applications. The proposed GA optimizes item selection based on value while ensuring that both capacity and risk constraints are met. Experimental results validate the GA's effectiveness, producing optimal solutions with maximum value and controlled risk thresholds.