Minimize Surprise MAP-Elites: A Task-Independent MAP-Elites Variant for Swarms

Tanja Katharina Kaiser, Heiko Hamann


Swarm robotics controllers are often automatically generated using methods of evolutionary computation with a task-specific fitness function to guide the optimization process. By contrast, our minimize surprise approach uses a task-independent fitness function to generate diverse behaviors over several independent evolutionary runs. Alternatives are divergent search algorithms rewarding behavioral novelty, such as novelty search, and quality-diversity algorithms generating diverse high-quality solutions, such as MAP-Elites. These approaches usually rely on task-dependent measures. We propose Minimize Surprise MAP-Elites, a task-independent MAP-Elites variant that combines MAP-Elites with our minimize surprise approach. Our first experiments result in high-quality solutions that lead to behavioral diversity across tasks and within tasks.

Original languageGerman
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion
Place of PublicationNY, USA
PublisherAssociation for Computing Machinery
Publication date09.07.2022
ISBN (Print)978-1-4503-9268-6
Publication statusPublished - 09.07.2022
EventGECCO '22: Genetic and Evolutionary Computation Conference - Boston, United States
Duration: 09.07.202213.07.2022

Cite this