Varying Difficulty of Knowledge Reuse in Benchmarks for Evolutionary Knowledge Transfer

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{scott:2024:CEC,

• author = "Eric O. Scott and Kenneth A. {De Jong}",
• title = "Varying Difficulty of Knowledge Reuse in Benchmarks for Evolutionary Knowledge Transfer",
• 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, cartesian genetic programming, Costs, Sociology, Evolutionary computation, Benchmark testing, Programming, Task analysis, transfer learning, transfer optimization",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10612149",
• 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.",
• notes = "also known as \cite{10612149}

  WCCI 2024",

}

Genetic Programming entries for Eric O Scott Kenneth De Jong

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