Dynamic programming is a classical optimization technique that systematically decomposes a complex problem into simpler sub-problems to find an optimal solution. We explore the use of bio-inspired ...

Learn how to solve the knapsack problem with two common approaches: dynamic programming and greedy methods. Compare their pros and cons and decide which one to use.

This paper investigates the reasoning capabilities of various Graph Neural Networks (GNN) on dynamic programming-based tasks, focusing on the impact of different aggregation strategies. The study ...

This repository contains the Knapsack problem solver using dynamic programming in python. Under the instances folder there are multiple example files to test given different amount of objects (n) to ...

First, the 0 - 1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is ...