A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
This work addresses a specific combinatorial optimization problem for researchers and practitioners in operations research or computer science, but it appears incremental as it builds on existing genetic algorithm approaches.
The paper tackled the 0-1 knapsack problem by developing a novel multi-objective genetic algorithm, and the result showed that it outperformed existing methods like the greedy algorithm and mixed strategy genetic algorithms in experiments.
The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms are able to find solutions as good as approximation algorithms do. This paper presents a novel multi-objective optimisation genetic algorithm for solving the 0-1 knapsack problem. Experiment results show that the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm.