Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization

We propose a novel method for automated algorithm selection in the domain of single-objective continuous black-box optimization. In contrast to existing methods, we use convolutional neural networks as the selection apparatus which bases its decision on a so-called 'fitness map'. This fitn...

Full description

Bibliographic Details

Published in:	2021 IEEE Symposium Series on Computational Intelligence (SSCI) pp. 1 - 8
Main Authors:	Prager, Raphael Patrick, Vinzent Seiler, Moritz, Trautmann, Heike, Kerschke, Pascal
Format:	Conference Proceeding
Language:	English
Published:	IEEE 05.12.2021
Subjects:	Automated Algorithm Configuration Benchmark testing Black-Box Optimization CMA-ES Convolutional neural networks Deep Learning Feature-Free Gray-scale Performance gain Point cloud compression Reinforcement learning Two dimensional displays
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!