A hybrid evolutionary approach for set packing problem

Chaurasia, Sachchida Nand; Sundar, Shyam; Singh, Alok

doi:10.1007/s12597-014-0184-3

Cited by 6 publications

(21 citation statements)

References 11 publications

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“…Further, in [16] and [34], two different versions of ACO were proposed for the SPP, and both were tested on the railway problem instances. The state-of-the-art approach for solving the SPP is an evolutionary algorithm (EA) with guided mutation (GM) (i.e., EA/G) [11]. The EA/G approach is considered as a cross between traditional genetic algorithm and estimation of distribution algorithm.…”

Section: Survey On the Set Packing Problem And Related Problemsmentioning

confidence: 99%

“…More recently, several modified and improved versions of EA/G were developed and tested on a different domain of combinatorial optimization problems. In the study in [11], an improved version of EA/G was proposed to solve the SPP. The problem domain knowledge is used to initialize the probability of objects in the set.…”

Section: Overview Of Evolutionary Algorithm With Guided Mutation (Ea/g)mentioning

confidence: 99%

“…In [10], the authors modified the original EA/G and applied it to an order acceptance and scheduling problem, which is a subset and permutation problem. For detailed study, please refer to [24,11,8,10,9].…”

Section: Overview Of Evolutionary Algorithm With Guided Mutation (Ea/g)mentioning

confidence: 99%

“…One_one swap heuristic is a modified version of the 1-1 exchange local search presented in [11]. It also operates on the first improvement strategy.…”

Section: One_one Swap Heuristic (Lh_6)mentioning

confidence: 99%

“…One_two swap heuristic is a modified version of the 1-2 exchange local search presented in [11]. Similar to One_one swap heuristic, function Find 1_2 (I * ) identifies two objects, say i and j, in set I * that are not in solution S p and do not violate the feasibility constraint.…”

Section: One_two Swap Heuristic (Lh_6)mentioning

confidence: 99%

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

Chaurasia

Kim

2019

Information Sciences

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights• An evolutionary algorithm based hyper-heuristic is proposed for the set packing problem (SPP) and the minimum weight dominating set (MWDS) problem.• Self-learning concept is employed in hyper-heuristic for a selection of heuristic.• Dynamic selection of parameter is adopted to make the approach, as much as possible, less dependent on parameter values. The empirical study shows the effectiveness of Dynamic selection of parameter.• The proposed approach has been compared with the respective state-of-the-art approaches for both the SPP and MWDS problem.• Computational results show the superiority of the proposed approach.

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Section: Survey On the Set Packing Problem And Related Problemsmentioning

confidence: 99%

Section: Overview Of Evolutionary Algorithm With Guided Mutation (Ea/g)mentioning

confidence: 99%

Section: Overview Of Evolutionary Algorithm With Guided Mutation (Ea/g)mentioning

confidence: 99%

“…One_one swap heuristic is a modified version of the 1-1 exchange local search presented in [11]. It also operates on the first improvement strategy.…”

Section: One_one Swap Heuristic (Lh_6)mentioning

confidence: 99%

Section: One_two Swap Heuristic (Lh_6)mentioning

confidence: 99%

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