Evolving genetic algorithm for Job Shop Scheduling problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@TechReport{werner:2000:rep1,

• author = "James C. Werner and Mehmet E. Aydin and Terence C. Fogarty",
• title = "Evolving genetic algorithm for Job Shop Scheduling problems",
• institution = "London South Bank University",
• year = "2001",
• address = "School of Computing, Information Systems and Mathematics, South Bank University, 103 Borough Road, London SE1 0AA, UK",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.geocities.com/jamwer2002/rep1.pdf",
• size = "5 pages",
• abstract = "an attempt to evolve genetic algorithms by a particular genetic programming method to make it able to solve the classical Job Shop Scheduling problem (JSSP), which is a type of very well known hard combinatorial optimisation problems. The aim is to look for a better GA such that solves JSSP with preferable scores. This looking up procedure is done by evolving GA with GP. First we solve a set of job shop scheduling benchmarks by using a conventional GA and then an association of GP to evolve a GA. The instance of JSSP tackled are available in OR literature.",

}

Genetic Programming entries for James Cunha Werner Mehmet Emin Aydin Terence C Fogarty

Citations