Cartesian Genetic Programming Hyper-Heuristic with Parameter Configuration for Production Lot-Sizing

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

@InProceedings{deAraujoPessoa:2020:CEC,

• author = "Luis Filipe {de Araujo Pessoa} and Bernd Hellingrath and Fernando Buarque {de Lima Neto}",
• title = "Cartesian Genetic Programming Hyper-Heuristic with Parameter Configuration for Production Lot-Sizing",
• booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
• year = "2020",
• editor = "Yaochu Jin",
• pages = "paper id24522",
• address = "internet",
• month = "19-24 " # jul,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Heuristic algorithms, Sociology, Statistics, Task analysis, Classification algorithms, Hyper-heuristics, Algorithm Generation, Parameter Configuration, Lot-sizing Problem",
• isbn13 = "978-1-7281-6929-3",
• DOI = "doi:10.1109/CEC48606.2020.9185602",
• abstract = "The design of well-performing heuristic-based algorithms is a tedious and time-consuming task. For this purpose, several approaches to automate the algorithm design have been investigated over the last years. Among them, Genetic Programming-based Hyper-Heuristics (GPHH) are able to design algorithms of varying lengths and permutations of algorithm's operators. Although the parametrization of generated algorithms is crucial for further adaptation of its behavior to the underlying problems, it has been neglected or only considered to a limited extent in many applications of GPHHs. This work proposes a way to integrate GPHH with parameter configuration. In particular, a Cartesian Genetic Programming Hyper-Heuristic (CGPHH) is developed. The proposed method is applied to the multi-level capacitated production lot-sizing problem, a complex and relevant problem in production planning. Conducted experiments evaluate both its efficacy and the quality of the generated algorithms. Finally, results are compared to the CGPHH without parameter configuration and a human-designed algorithm, indicating the ability of CGPHH-PC to generate algorithms that have the best overall-performance for different cutoff times.",
• notes = "https://wcci2020.org/

  University of Muesnter, Germany; University of Muenster, Germany; University of Pernambuco, Brazil.

  Also known as \cite{9185602}",

}

Genetic Programming entries for Luis Filipe de Araujo Pessoa Bernd Hellingrath Fernando Buarque de Lima Neto

Citations