Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{he:2024:CEC,
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author = "Yifan He and Claus Aranha",
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title = "Evolving Benchmark Functions to Compare Evolutionary
Algorithms via Genetic Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Measurement,
Correlation, Evolutionary computation, Switches,
Benchmark testing, Generators, Benchmark functions,
Algorithmic behavior, MAP-Elites",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611801",
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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.",
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notes = "also known as \cite{10611801}
WCCI 2024",
- }
Genetic Programming entries for
Yifan He
Claus de Castro Aranha
Citations