Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features

Prager Patrick Raphael , Dietrich Konstantin , Schneider Lennart , Schäpermeier Lennart , Bischl Bernd , Kerschke Pascal , Trautmann Heike , Mersmann Olaf

Keywords

Exploratory Landscape Analysis; Benchmarking; Instance Generator; Black-Box Continuous Optimization; Neural Networks

Cite as

Prager Patrick Raphael &mdash, , Dietrich Konstantin &mdash, , Schneider Lennart &mdash, , Schäpermeier Lennart &mdash, , Bischl Bernd &mdash, , Kerschke Pascal &mdash, , Trautmann Heike &mdash, , & Mersmann Olaf, (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In Chicano, F., Friedrich, T., Kötzing, T., & Rothlauf, F. (Eds.), FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 129–139). online: ACM Press.

Details

Publication type
Research article in proceedings (conference)

Peer reviewed
Yes

Publication status
Published

Year
2023

Conference
17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms

Venue
Potsdam

Book title
FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms

Editor
Chicano, Francisco; Friedrich, Tobias; Kötzing, Timo; Rothlauf, Franz

Start page
129

End page
139

Publisher
ACM Press

Place
online

DOI
https://doi.org/10.1145/3594805.3607136

Full text
https://doi.org/