Automating Knowledge Transfer with Multi-Task Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Scott:2019:CEC,

• author = "Eric O. Scott and Kenneth A. {De Jong}",
• title = "Automating Knowledge Transfer with Multi-Task Optimization",
• booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2019",
• pages = "2252--2259",
• month = jun,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2019.8790224",
• abstract = "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.",
• notes = "Also known as \cite{8790224}",

}

Genetic Programming entries for Eric O Scott Kenneth De Jong

Citations