Towards Merging Binary Integer Programming Techniques with Genetic Algorithms
Author
Source
Advances in Operations Research
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-17
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper presents a framework based on merging a binary integer programming technique with a genetic algorithm.
The framework uses both lower and upper bounds to make the employed mathematical formulation of a problem as tight as possible.
For problems whose optimal solutions cannot be obtained, precision is traded with speed through substituting the integrality constrains in a binary integer program with a penalty.
In this way, instead of constraining a variable u with binary restriction, u is considered as real number between 0 and 1, with the penalty of Mu(1-u), in which M is a large number.
Values not near to the boundary extremes of 0 and 1 make the component of Mu(1-u) large and are expected to be avoided implicitly.
The nonbinary values are then converted to priorities, and a genetic algorithm can use these priorities to fill its initial pool for producing feasible solutions.
The presented framework can be applied to many combinatorial optimization problems.
Here, a procedure based on this framework has been applied to a scheduling problem, and the results of computational experiments have been discussed, emphasizing the knowledge generated and inefficiencies to be circumvented with this framework in future.
American Psychological Association (APA)
Zamani, Reza. 2017. Towards Merging Binary Integer Programming Techniques with Genetic Algorithms. Advances in Operations Research،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1125238
Modern Language Association (MLA)
Zamani, Reza. Towards Merging Binary Integer Programming Techniques with Genetic Algorithms. Advances in Operations Research No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1125238
American Medical Association (AMA)
Zamani, Reza. Towards Merging Binary Integer Programming Techniques with Genetic Algorithms. Advances in Operations Research. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1125238
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125238