Varying Difficulty of Knowledge Reuse in Benchmarks for Evolutionary Knowledge Transfer
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @InProceedings{scott:2024:CEC,
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author = "Eric O. Scott and Kenneth A. {De Jong}",
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title = "Varying Difficulty of Knowledge Reuse in Benchmarks
for Evolutionary Knowledge Transfer",
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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, cartesian
genetic programming, Costs, Sociology, Evolutionary
computation, Benchmark testing, Programming, Task
analysis, transfer learning, transfer optimization",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612149",
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abstract = "Evolutionary knowledge transfer (EKT) has emerged in
the past few years as a new genre of strategy for
equipping search and optimisation algorithms with prior
knowledge. Most EKT research, however, has been
dominated by a small number of narrow benchmark
functions that do not reflect the diversity of ways
problems may be related in different domains. In this
paper, we present a preliminary step toward good
benchmark design for evolutionary knowledge transfer,
by examining four simple benchmark task sets. These
cover real-valued, modularly varying goals, and Boolean
circuit synthesis domains (where we use Cartesian
genetic programming for the latter). We perform
experiments with different source-task target pairs
drawn from these domains, where we measure the degree
to which transfer is successful. Results with a basic
population-seeding transfer strategy show that these
domain benchmarks illustrate a continuum of difficulty
for knowledge reuse, and that a many-source transfer
approach that uses population seeding as a form of
source selection can dramatically reduce the cost of
finding the best knowledge source for a given target
task. We anticipate that benchmarks such as these can
help the community get a stronger understanding of when
and why knowledge transfer is useful, and how problem
similarities can best be exnloited.",
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notes = "also known as \cite{10612149}
WCCI 2024",
- }
Genetic Programming entries for
Eric O Scott
Kenneth De Jong
Citations