- author = "Ezequiel Castellano and Ahmet Cetinkaya and Cedric Ho Thanh and Stefan Klikovits and Xiaoyi Zhang and Paolo Arcaini",
- title = "{Frenetic} at the {SBST} 2021 Tool Competition",
- booktitle = "2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST)",
- year = "2021",
- editor = "Jie M Zhang and Erik Fredericks",
- pages = "36--37",
- address = "internet",
- month = "31 " # may,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-6654-4571-9/21/",
-
URL = "
https://raw.githubusercontent.com/ERATOMMSD/frenetic-sbst21/main/src/frenetic-sbst21-preprint.pdf",
-
code_url = "
https://github.com/ERATOMMSD/frenetic-sbst21",
-
video_url = "https://mmm-www-videos.s3-ap-northeast-1.amazonaws.com/MMMSeminar/etc/2021_SBST_Frenetic/frenetic_talk.mp4",
-
DOI = "
doi:10.1109/SBST52555.2021.00016",
- size = "2 pages",
- abstract = "Frenetic is a genetic approach that leverages a curvature-based road representation. Given an autonomous driving agent, the goal of Frenetic is to generate roads where the agent fails to stay within its lane. In other words, Frenetic tries to minimise the out of bound distance, which is the distance between the car and either edge of the lane if the car is within the lane, and proceeds to negative values once the car drives off. This resembles classic aspects of genetic algorithms such as mutations and crossover, but introduces some nuances aiming at improving diversity of the generated roads.",
-
notes = "National Institute of Informatics, Tokyo,
Japan
https://sbst21.github.io/program/",
