### Abstract

Utilizing knowledge of the problem of interest and lessons learned from solving similar problems would help to find the final optimal solution of better quality. A hyper-heuristic algorithm is to gain an advantage of such process. In this paper, we present an evolutionary algorithm based hyper-heuristic framework for solving the set packing problem (SPP). The SPP is a typical NP-hard problem. The hyper-heuristic is comprising of high level and low level. The higher level is mainly engaged in generating or constructing a heuristic. An evolutionary algorithm with guided mutation (EA/G) is employed at the high level. Whereas a set of problem-independent and problem-specific heuristics, called low level heuristics, are employed at the low level of hyper-heuristic. EA/G is recently added to the class of the evolutionary algorithms that try to utilize the complementary characteristics of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) to generate new offspring. In EA/G, the guided mutation operator generates an offspring by sampling the probability vector. The proposed approach is compared with the state-of-the-art approaches reported in the literature. The computational results show the effectiveness of the proposed approach.

Original language | English |
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Title of host publication | Harmony Search and Nature Inspired Optimization Algorithms - Theory and Applications, ICHSA 2018 |

Editors | Jagdish Chand Bansal, Joong Hoon Kim, Anupam Yadav, Kusum Deep, Neha Yadav |

Publisher | Springer Verlag |

Pages | 259-268 |

Number of pages | 10 |

ISBN (Print) | 9789811307607 |

DOIs | |

Publication status | Published - 2019 Jan 1 |

Event | 4th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2018 - Gurgaon, India Duration: 2018 Feb 7 → 2018 Feb 9 |

### Publication series

Name | Advances in Intelligent Systems and Computing |
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Volume | 741 |

ISSN (Print) | 2194-5357 |

### Conference

Conference | 4th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2018 |
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Country | India |

City | Gurgaon |

Period | 18/2/7 → 18/2/9 |

### Fingerprint

### Keywords

- Constrained optimization
- Estimation of distribution algorithm
- Genetic algorithm
- Guided mutation
- Heuristic
- Hyper-heuristic
- Set packing problem

### ASJC Scopus subject areas

- Control and Systems Engineering
- Computer Science(all)

### Cite this

*Harmony Search and Nature Inspired Optimization Algorithms - Theory and Applications, ICHSA 2018*(pp. 259-268). (Advances in Intelligent Systems and Computing; Vol. 741). Springer Verlag. https://doi.org/10.1007/978-981-13-0761-4_26

**An evolutionary algorithm based hyper-heuristic for the set packing problem.** / Chaurasia, Sachchida Nand; Jung, Donghwi; Lee, Ho Min; Kim, Joong Hoon.

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

*Harmony Search and Nature Inspired Optimization Algorithms - Theory and Applications, ICHSA 2018.*Advances in Intelligent Systems and Computing, vol. 741, Springer Verlag, pp. 259-268, 4th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2018, Gurgaon, India, 18/2/7. https://doi.org/10.1007/978-981-13-0761-4_26

}

TY - GEN

T1 - An evolutionary algorithm based hyper-heuristic for the set packing problem

AU - Chaurasia, Sachchida Nand

AU - Jung, Donghwi

AU - Lee, Ho Min

AU - Kim, Joong Hoon

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Utilizing knowledge of the problem of interest and lessons learned from solving similar problems would help to find the final optimal solution of better quality. A hyper-heuristic algorithm is to gain an advantage of such process. In this paper, we present an evolutionary algorithm based hyper-heuristic framework for solving the set packing problem (SPP). The SPP is a typical NP-hard problem. The hyper-heuristic is comprising of high level and low level. The higher level is mainly engaged in generating or constructing a heuristic. An evolutionary algorithm with guided mutation (EA/G) is employed at the high level. Whereas a set of problem-independent and problem-specific heuristics, called low level heuristics, are employed at the low level of hyper-heuristic. EA/G is recently added to the class of the evolutionary algorithms that try to utilize the complementary characteristics of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) to generate new offspring. In EA/G, the guided mutation operator generates an offspring by sampling the probability vector. The proposed approach is compared with the state-of-the-art approaches reported in the literature. The computational results show the effectiveness of the proposed approach.

AB - Utilizing knowledge of the problem of interest and lessons learned from solving similar problems would help to find the final optimal solution of better quality. A hyper-heuristic algorithm is to gain an advantage of such process. In this paper, we present an evolutionary algorithm based hyper-heuristic framework for solving the set packing problem (SPP). The SPP is a typical NP-hard problem. The hyper-heuristic is comprising of high level and low level. The higher level is mainly engaged in generating or constructing a heuristic. An evolutionary algorithm with guided mutation (EA/G) is employed at the high level. Whereas a set of problem-independent and problem-specific heuristics, called low level heuristics, are employed at the low level of hyper-heuristic. EA/G is recently added to the class of the evolutionary algorithms that try to utilize the complementary characteristics of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) to generate new offspring. In EA/G, the guided mutation operator generates an offspring by sampling the probability vector. The proposed approach is compared with the state-of-the-art approaches reported in the literature. The computational results show the effectiveness of the proposed approach.

KW - Constrained optimization

KW - Estimation of distribution algorithm

KW - Genetic algorithm

KW - Guided mutation

KW - Heuristic

KW - Hyper-heuristic

KW - Set packing problem

UR - http://www.scopus.com/inward/record.url?scp=85053282047&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053282047&partnerID=8YFLogxK

U2 - 10.1007/978-981-13-0761-4_26

DO - 10.1007/978-981-13-0761-4_26

M3 - Conference contribution

AN - SCOPUS:85053282047

SN - 9789811307607

T3 - Advances in Intelligent Systems and Computing

SP - 259

EP - 268

BT - Harmony Search and Nature Inspired Optimization Algorithms - Theory and Applications, ICHSA 2018

A2 - Bansal, Jagdish Chand

A2 - Kim, Joong Hoon

A2 - Yadav, Anupam

A2 - Deep, Kusum

A2 - Yadav, Neha

PB - Springer Verlag

ER -