Designing Black-Box Optimizers with PushGP

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

@InProceedings{stanovov:2024:GECCOcomp,

• author = "Vladimir Stanovov",
• title = "Designing {Black-Box} Optimizers with {PushGP}",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart",
• pages = "535--538",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, hyper-heuristic, numerical optimization: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654172",
• size = "4 pages",
• abstract = "This study is focused on an attempt to design general-purpose black-box population-based numerical optimization algorithms from scratch using the Push language and a genetic programming approach. The designed Push programs are capable of performing operations on vectors, thus allowing creating algorithms suitable for problems of any dimension. The programs are applied to each of the individuals of the population, and the interaction between individuals is possible. The training is performed on the single-objective benchmark on numerical optimization presented within the Congress on Evolutionary Computation 2017. It is shown that with significant computational effort such approach is capable of creating unique optimization methods, which could be competitive to some of the known optimization tools.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Vladimir Stanovov

Citations