Solving large knapsack problems with a genetic algorithm

Author

Spillman, Richard

Author_Institution

Dept. of Comput. Sci., Pacific Lutheran Univ., Tacoma, WA, USA

Volume

1

fYear

1995

fDate

22-25 Oct 1995

Firstpage

632

Abstract

This paper develops a new approach to finding solutions to the subset sum problem. The subset sum problem is an important NP-complete problem in computer science which has applications in operations research, cryptography, and bin packing. A genetic algorithm is developed which easily solves this problem. The genetic algorithm begins with a randomly generated population of solutions and breeds a new population using the best elements of the previous population. Each generation of solutions produces better solutions to the subset-sum problem than the previous generation. It is shown that this approach will efficiently produce solutions to large (10,000 elements or more) subset sum problems. Various parameters of the algorithm are varied in order to improve its performance

Keywords

computational complexity; genetic algorithms; operations research; NP-complete problem; bin packing; cryptography; genetic algorithm; large knapsack problems; operations research; subset sum problem; Application software; Computer science; Genetic algorithms; Greedy algorithms; Humans; Machine learning; Machine learning algorithms; Operations research; Public key cryptography; Space power stations;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.537834

Filename

537834