# Addressing The Knapsack Challenge Through Cultural Algorithm Optimization

arXiv: https://arxiv.org/abs/2401.03324 · HTML: https://mlsanity.com/paper/2401.03324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2023.

## Built on by (most cited first, 1 of 1)

- [Brain Tumor Classification using Vision Transformer with Selective Cross-Attention Mechanism and Feature Calibration](https://mlsanity.com/paper/2406.17670/) (2024)

## Abstract (arXiv)

The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints.