Title :
Solving large knapsack problems with a genetic algorithm
Author :
Spillman, Richard
Author_Institution :
Dept. of Comput. Sci., Pacific Lutheran Univ., Tacoma, WA, USA
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;
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
DOI :
10.1109/ICSMC.1995.537834
