On the Application of e-Lexicase Selection in the Generation of Dispatching Rules

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

@InProceedings{Planinic:2021:CEC,

• author = "Lucija Planinic and Marko Durasevic and Domagoj Jakobovic",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "On the Application of e-Lexicase Selection in the Generation of Dispatching Rules",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "2125--2132",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• keywords = "genetic algorithms, genetic programming, Training, Sociology, Wheels, Dynamic scheduling, Dispatching, Steady-state, lexicase, dispatching rules, scheduling",
• isbn13 = "978-1-7281-8393-0",
• DOI = "doi:10.1109/CEC45853.2021.9504982",
• abstract = "Dynamic online scheduling is a difficult problem which commonly appears in the real world. This is because the decisions have to be performed in a small amount of time using only currently available incomplete information. In such cases dispatching rules (DRs) are the most commonly used methods. Since designing them manually is a difficult task, this process has been successfully automatised by using genetic programming (GP). The quality of the evolved rules depends on the problem instances that are used during the training process. Previous studies demonstrated that careful selection of problem instances on which the solutions should be evaluated during evolution improves the performance of the generated rules. This paper examines the application of the epsilon-lexicase selection to the design of DRs for the unrelated machines scheduling. This selection offers a better solution diversity since the individuals are selected based on a smaller subset of instances, which leads to the creation of DRs that perform well on the selected instances. The experiments demonstrate that this type of selection can significantly improve the results for the Roulette Wheel and Elimination GP variants, while achieving the same performance as the Steady State Tournament GP. Furthermore, the epsilon-lexicase based algorithms have a better convergence rate, which means that the increased diversity in the population has a positive effect on the evolution process.",
• notes = "Also known as \cite{9504982}",

}

Genetic Programming entries for Lucija Planinic Marko Durasevic Domagoj Jakobovic

Citations