Interactive Evolution of Complex Behaviours Through Skill Encapsulation
- Pablo González de Prado Salas,
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewHost publication Subtitle
European Conference on the Applications of Evolutionary ComputationOriginal language
EnglishPages from-to (Number of pages)
Pages 853-869Publication milestones
- Published - 2017
Publication status
Published - 2017
Publisher
Springer, United States, GermanyBook series
- Book series name: Lecture Notes in Computer Science
Volume: 10199
ISSN: 0302-9743
ISBN (Print)
978-3-319-55848-6ISBN (Electronic)
978-3-319-55849-3Publication IDs
- Scopus: 85017541047
Host publication title
Applications of Evolutionary Computation. EvoApplications 2017Host publication editors
- Giovanni Squillero
- Kevin Sim
Abstract
Human-based computation (HBC) is an emerging research area in which humans and machines collaborate to solve tasks that neither one can solve in isolation. In evolutionary computation, HBC is often realized through interactive evolutionary computation (IEC), in which a user guides evolution by iteratively selecting the parents for the next generation. IEC has shown promise in a variety of different domains, but evolving more complex or hierarchically composed behaviours remains challenging with the traditional IEC approach. To overcome this challenge, this paper combines the recently introduced ESP (encapsulation, syllabus and pandemonium) algorithm with IEC to allow users to intuitively break complex challenges into smaller pieces and preserve, reuse and combine interactively evolved sub-skills. The combination of ESP principles with IEC provides a new way in which human insights can be leveraged in evolutionary computation and, as the results in this paper show, IEC-ESP is able to solve complex control problems that are challenging for a traditional fitness-based approach.
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Accepted author manuscript
Accepted author manuscript, 3.29 MB
