Year: 2023
Type: conference-paper
Abstract: The field of evolutionary knowledge transfer (EKT) has recently begun to systematically develop algorithms that exploit a number of related problem instances to accelerate problem solving on difficult optimization tasks. EKT has the potential to have a major impact on evolutionary computation practice, comparable to the role that neural network pretraining has had on machine learning. But the community has only scratched the surface of the theoretical hurdles that the knowledge-transfer workflow raises. We introduce a three-part collect-select-exploit framework for understanding EKT, which we use to highlight the need for better evaluation and benchmarking approaches for transfer. We then present some of the first analytical results for EKT, proving no-free-lunch theorems for transfer, and proving what is (to our knowledge) the first asymptotic runtime result for transfer optimization. These results can serve as the basis for future research into the strengths and limitations of knowledge transfer as an optimization paradigm, and they serve to emphasize the need for more comprehensive benchmarks to hone progress in the field.
Authors: Eric O. Scott, Kenneth A. De Jong
Institutions: Mitre (United States), George Mason University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4385438065