Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{he:2024:CEC,

• author = "Yifan He and Claus Aranha",
• title = "Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Measurement, Correlation, Evolutionary computation, Switches, Benchmark testing, Generators, Benchmark functions, Algorithmic behavior, MAP-Elites",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611801",
• abstract = "In this study, we use Genetic Programming (GP) to compose new optimisation benchmark functions. Optimisation benchmarks have the important role of showing the differences between evolutionary algorithms, making it possible for further analysis and comparisons. We show that the benchmarks generated by GP are able to differentiate algorithms better than human-made benchmark functions. The fitness measure of the GP is the Wasserstein distance of the solutions found by a pair of optimisers. Additionally, we use MAP-Elites to both enhance the search power of the GP and also illustrate how the difference between optimisers changes by various landscape features. Our approach provides a novel way to automate the design of benchmark functions and to compare evolutionary algorithms.",
• notes = "also known as \cite{10611801}

  WCCI 2024",

}

Genetic Programming entries for Yifan He Claus de Castro Aranha

Citations