### Abstract

This paper describes a hybrid algorithm to solve the 0-1 Knapsack Problem using the Genetic Algorithm combined with Rough Set Theory. The Knapsack problem is a combinatorial optimization problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. There are other ways to solve this problem, namely Dynamic Programming and Greedy Method, but they are not very efficient. The complexity of Dynamic approach is of the order of O(n^{3}) whereas the Greedy Method doesn't always converge to an optimum solution^{[2]}. The Genetic Algorithm provides a way to solve the knapsack problem in linear time complexity^{[2]}. The attribute reduction technique which incorporates Rough Set Theory finds the important genes, hence reducing the search space and ensures that the effective information will not be lost. The inclusion of Rough Set Theory in the Genetic Algorithm is able to improve its searching efficiency and quality.

Original language | English |
---|---|

Title of host publication | Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 1120-1125 |

Number of pages | 6 |

ISBN (Electronic) | 9781479939145 |

DOIs | |

Publication status | Published - 23-01-2015 |

Event | 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014 - Ramanathapuram, Tamil Nadu, India Duration: 08-05-2014 → 10-05-2014 |

### Conference

Conference | 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014 |
---|---|

Country | India |

City | Ramanathapuram, Tamil Nadu |

Period | 08-05-14 → 10-05-14 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Networks and Communications
- Control and Systems Engineering

### Cite this

*Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014*(pp. 1120-1125). [7019272] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCCT.2014.7019272

}

*Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014.*, 7019272, Institute of Electrical and Electronics Engineers Inc., pp. 1120-1125, 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014, Ramanathapuram, Tamil Nadu, India, 08-05-14. https://doi.org/10.1109/ICACCCT.2014.7019272

**Solving the 0-1 Knapsack problem using genetic algorithm and rough set theory.** / Pradhan, Tribikram; Israni, Akash; Sharma, Manish.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Solving the 0-1 Knapsack problem using genetic algorithm and rough set theory

AU - Pradhan, Tribikram

AU - Israni, Akash

AU - Sharma, Manish

PY - 2015/1/23

Y1 - 2015/1/23

N2 - This paper describes a hybrid algorithm to solve the 0-1 Knapsack Problem using the Genetic Algorithm combined with Rough Set Theory. The Knapsack problem is a combinatorial optimization problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. There are other ways to solve this problem, namely Dynamic Programming and Greedy Method, but they are not very efficient. The complexity of Dynamic approach is of the order of O(n3) whereas the Greedy Method doesn't always converge to an optimum solution[2]. The Genetic Algorithm provides a way to solve the knapsack problem in linear time complexity[2]. The attribute reduction technique which incorporates Rough Set Theory finds the important genes, hence reducing the search space and ensures that the effective information will not be lost. The inclusion of Rough Set Theory in the Genetic Algorithm is able to improve its searching efficiency and quality.

AB - This paper describes a hybrid algorithm to solve the 0-1 Knapsack Problem using the Genetic Algorithm combined with Rough Set Theory. The Knapsack problem is a combinatorial optimization problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. There are other ways to solve this problem, namely Dynamic Programming and Greedy Method, but they are not very efficient. The complexity of Dynamic approach is of the order of O(n3) whereas the Greedy Method doesn't always converge to an optimum solution[2]. The Genetic Algorithm provides a way to solve the knapsack problem in linear time complexity[2]. The attribute reduction technique which incorporates Rough Set Theory finds the important genes, hence reducing the search space and ensures that the effective information will not be lost. The inclusion of Rough Set Theory in the Genetic Algorithm is able to improve its searching efficiency and quality.

UR - http://www.scopus.com/inward/record.url?scp=84923313662&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923313662&partnerID=8YFLogxK

U2 - 10.1109/ICACCCT.2014.7019272

DO - 10.1109/ICACCCT.2014.7019272

M3 - Conference contribution

AN - SCOPUS:84923313662

SP - 1120

EP - 1125

BT - Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -