Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.
|Title of host publication||GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion|
|Place of Publication||New York, USA|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 6 Jul 2018|
|Event||2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan|
Duration: 15 Jul 2018 → 19 Jul 2018
|Conference||2018 Genetic and Evolutionary Computation Conference, GECCO 2018|
|Period||15/07/18 → 19/07/18|
Bibliographical noteFunding Information:
This work is supported by Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313].
© 2018 Copyright held by the owner/author(s).
- Algorithm based landscape feature
- Algorithm selection
- Evolutionary algorithm
- Exploratory landscape analysis