Automating Knowledge Transfer with Multi-Task Optimization
Eric O. Scott, Kenneth Alan De Jong · 2019
Algorithmic knowledge transfer can make difficult problems easier to solve well. Most existing work on knowledge transfer for optimization relies on humans to manually select source tasks to transfer information from, and attempts to automate the source selection process are few and far apart. In this work, we survey the existing methods that have been devised for knowledge transfer in evolutionary algorithms, and we present an experimental approach to automated source task selection based on a multi-task implementation of Cartesian genetic programming (MTCGP). Our experiments indicate that this strategy outperforms single-task CGP on solving a set of Boolean function synthesis tasks. We further develop a mutation weighting scheme aimed at protecting useful components from destructive mutation.