Varying Difficulty of Knowledge Reuse in Benchmarks for Evolutionary Knowledge Transfer

Evolutionary knowledge transfer (EKT) has emerged in the past few years as a new genre of strategy for equipping search and optimization 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.