Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Masood:2018:evocop,

• author = "Atiya Masood and Gang Chen2 and Yi Mei and Mengjie Zhang",
• title = "Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling",
• booktitle = "The 18th European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2018",
• year = "2018",
• editor = "Arnaud Liefooghe and Manuel Lopez-Ibanez",
• series = "LNCS",
• volume = "10782",
• publisher = "Springer",
• pages = "116--131",
• address = "Parma, Italy",
• month = "4-6 " # apr,
• organisation = "Species",
• keywords = "genetic algorithms, genetic programming, Job Shop Scheduling, Many-objective optimization, Reference points",
• isbn13 = "978-3-319-77448-0",
• DOI = "doi:10.1007/978-3-319-77449-7_8",
• abstract = "Job Shop Scheduling (JSS) is considered to be one of the most significant combinatorial optimization problems in practice. It is widely evidenced in the literature that JSS usually contains many (four or more) potentially conflicting objectives. One of the promising and successful approaches to solve the JSS problem is Genetic Programming Hyper-Heuristic (GP-HH). This approach automatically evolves dispatching rules for solving JSS problems. This paper aims to evolve a set of effective dispatching rules for many-objective JSS with genetic programming and NSGA-III. NSGA-III originally defines uniformly distributed reference points in the objective space. Thus, there will be few reference points with no Pareto optimal solutions associated with them; especially, in the cases with discrete and non-uniform Pareto front, resulting in many useless reference points during evolution. In other words, these useless reference points adversely affect the performance of NSGAIII and genetic programming. To address the above issue, in this paper a new reference point adaptation mechanism is proposed based on the distribution of the candidate solutions.We evaluated the performance of the proposed mechanism on many-objective benchmark JSS instances. Our results clearly show that the proposed strategy is promising in adapting reference points and outperforms the existing state-of-the-art algorithms for many-objective JSS.",
• notes = "EvoCOP2018 held in conjunction with EuroGP'2018 EvoMusArt2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evocop.php",

}

Genetic Programming entries for Atiya Masood Aaron Chen Yi Mei Mengjie Zhang

Citations