Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Masood:2022:CEC,

• author = "Atiya Masood and Gang Chen2 and Yi Mei and Harith Al-Sahaf and Mengjie Zhang",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• isbn13 = "978-1-6654-6708-7",
• abstract = "Job Shop Scheduling (JSS) is an important real-world problem. However, the problem is challenging because of many conflicting objectives and the complexity of production flows. Genetic programming-based hyper-heuristic (GP-HH) is a useful approach for automatically evolving effective dispatching rules for many-objective JSS. However, the evolved Pareto-front is highly irregular, seriously affecting the effectiveness of GP-HH. Although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it usually uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. In fact, some reference points may never be linked to any Pareto-optimal solutions, rendering them useless. These useless reference points can significantly impact the performance of any reference-point-based many-objective optimization algorithms such as NSGA-III. This paper proposes a new reference point adaption process that explicitly constructs the distribution model using Gaussian process to effectively reduce the number of useless reference points to a low level, enabling a close match between reference points and the distribution of Pareto-optimal solutions. We incorporate this mechanism into NSGA-III to build a new algorithm called MARP-NSGA-III which is compared experimentally to several popular many-objective algorithms. Experiment results on a large collection of many-objective benchmark JSS instances clearly show that MARP-NSGA-III can significantly improve the performance by using our Gaussian Process-based reference point adaptation mechanism.",
• keywords = "genetic algorithms, genetic programming, Adaptation models, Job shop scheduling, Processor scheduling, Sociology, Gaussian processes, Production, Maintenance engineering, Many-objective Optimization, Evolutionary Computation, Gaussian Process, Adaptive reference points, Job Shop Scheduling",
• DOI = "doi:10.1109/CEC55065.2022.9870322",
• notes = "Also known as \cite{9870322}",

}

Genetic Programming entries for Atiya Masood Aaron Chen Yi Mei Harith Al-Sahaf Mengjie Zhang

Citations