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Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem

Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi. Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi
10.5120/ijais2016451588
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  1. Chaouqi Mohsine, Benhra Jamal and My Ali El Oualidi. Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem. International Journal of Applied Information Systems 11(3):21-25, August 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451588,
    	author = "Chaouqi Mohsine and Benhra Jamal and My Ali El Oualidi",
    	title = "Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "21-25",
    	numpages = 5,
    	url = "http://www.ijais.org/archives/volume11/number3/926-2016451588",
    	doi = "10.5120/ijais2016451588",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

The present paper deals with the open-shop scheduling problem using a manual tuning of a genetic algorithm’s parameters. A comparison has been performed between Taillard’s Benchmarks for 60 instances, 2 dispatching rules and 198 variants from the GA algorithm obtained by changing the population size, the generation’s number, the crossover probability, and the mutation probability. Interesting results were obtained leading to some conclusions for the best choice of the parameters.

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Keywords

Scheduling, Open shop, Genetic Algorithms, Tuning, Benchmarks