Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling

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

@InProceedings{Panda:2021:CEC,

• author = "Sai Panda and Yi Mei",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "1848--1855",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• isbn13 = "978-1-7281-8393-0",
• abstract = "Genetic Programming (GP) has been successfully applied to evolve effective dispatching rules for Dynamic Job Shop Scheduling (DJSS). However, the GP-evolved rules are usually too complex and hard to interpret. In this paper, we aim to address this issue by evolving simpler rules without losing effectiveness. To this end, we develop a set of algebraic simplification operators based on our domain knowledge about dynamic scheduling, which can recursively convert a rule into a mathematically equivalent but simpler one. The algebraic simplification operators can guarantee that the individual stays equivalent before and after the simplification. Then, we develop a GP algorithm with these simplification operators. We compared the GP with the simplification operators with the baseline GP without simplification on a range of scheduling instances, and the results showed that using the algebraic simplification can slightly reduce the program size without sacrificing the test performance of the evolved dispatching rules. Furthermore, through deep analysis, we have also discovered the limitations of the pure algebraic simplification for GP to evolve DJSS dispatching rules, which can hardly simplify the individuals after the first generation.",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Heuristic algorithms, Evolutionary computation, Dynamic scheduling, Dispatching, Dynamic programming",
• DOI = "doi:10.1109/CEC45853.2021.9505010",
• notes = "Also known as \cite{9505010}",

}

Genetic Programming entries for Sai Panda Yi Mei

Citations