To bias or not to bias: Probabilistic initialisation for evolving dispatching rules

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Durasevic:2023:EuroGP,

• author = "Marko Durasevic and Francisco Javier Gil-Gala and Domagoj Jakobovic",
• title = "To bias or not to bias: Probabilistic initialisation for evolving dispatching rules",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "308--323",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Dispatching rules, Unrelated machines environment, Scheduling, Individual initialisation: Poster",
• isbn13 = "978-3-031-29572-0",
• URL = "https://rdcu.be/c8U3i",
• DOI = "doi:10.1007/978-3-031-29573-7_20",
• size = "16 pages",
• abstract = "he automatic generation of dispatching rules (DRs) for various scheduling problems using genetic programming (GP) has become an increasingly researched topic in recent years. Creating DRs in this way relieves domain experts of the tedious task of manually designing new rules, but also often leads to the discovery of better rules than those already available. However, developing new DRs is a computationally intensive process that takes time to converge to good solutions. One possible way to improve the convergence of evolutionary algorithms is to use a more sophisticated method to generate the initial population of individuals. In this paper, we propose a simple method for initialising individuals that uses probabilistic information from previously evolved DRs. The method extracts the information on how many times each node occurs at each level of the tree and in each context. This information is then used to introduce bias in the selection of the node to be selected at a particular position during the construction of the expression tree. The experiments show that with the proposed method it is possible to improve the convergence of GP when generating new DRs, so that GP can obtain high-quality DRs in a much shorter time.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Marko Durasevic Francisco Javier Gil Gala Domagoj Jakobovic

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