Learn how to design an algorithm to solve the knapsack problem, a classic optimization challenge in computer science, using dynamic programming, greedy method, branch and bound, and genetic algorithm.

Problem Definition The 0/1 knapsack problem is an optimization problem where you are given a set of items, each with a weight and a profit. The objective is to select a subset of items that maximizes ...

A dynamic knapsack problem is one where the items, their values, or the weight limit can change over time, but you can still access all the information at each step. For example, you might have a ...

Some genetic algorithms with 85% crossover, for example, will copy 15% of the old population to the new population. Mutation. The mutation function, like random mutations in nature, keeps our ...

In order to optimize the knapsack problem further, this paper proposes an innovative model based on dynamic expectation efficiency, and establishes a new optimization algorithm of 0-1 knapsack problem ...

The unbounded knapsack problem: given a knapsack of some capacity and a set of items that have a weight and a value, determine the maximum value of items you can place in your knapsack. The number ...

Theoretical results provide conditions under which merged cover inequalities are valid. Polynomial time algorithms are created to find merged cover inequalities. A computational study demonstrates ...