Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

07/20/2019
by   , et al.
0

The 0/1 multidimensional knapsack problem is the 0/1 knapsack problem with m constraints which makes it difficult to solve using traditional methods like dynamic programming or branch and bound algorithms. We present a genetic algorithm for the multidimensional knapsack problem with Java code that is able to solve publicly available instances in a very short computational duration. Our algorithm uses iteratively computed Lagrangian multipliers as constraint weights to augment the greedy algorithm for the multidimensional knapsack problem and uses that information in a greedy crossover in a genetic algorithm. The algorithm uses several other hyperparameters which can be set in the code to control convergence. Our algorithm improves upon the algorithm by Chu and Beasley in that it converges to optimum or near optimum solutions much faster.

READ FULL TEXT
research
04/24/2020

GKNAP: A Java and C++ package for solving the multidimensional knapsack problem

The 0/1 multidimensional (multi-constraint) knapsack problem is the 0/1 ...
research
09/19/2019

Learning Optimal and Near-Optimal Lexicographic Preference Lists

We consider learning problems of an intuitive and concise preference mod...
research
05/28/2013

A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures

We propose a cooperative coevolutionary genetic algorithm for learning B...
research
02/22/2013

On the performance of a hybrid genetic algorithm in dynamic environments

The ability to track the optimum of dynamic environments is important in...
research
05/12/2020

Simulated Annealing Algorithm for the Multiple Choice Multidimensional Knapsack Problem

The multiple choice multidimensional knapsack problem (MCMK) isa harder ...
research
04/06/2014

A Denoising Autoencoder that Guides Stochastic Search

An algorithm is described that adaptively learns a non-linear mutation d...
research
07/26/2021

A Partial Reproduction of A Guided Genetic Algorithm for Automated Crash Reproduction

This paper is a partial reproduction of work by Soltani et al. which pre...

Please sign up or login with your details

Forgot password? Click here to reset