Detail of the student project

Topic:Random embeddings for black-box optimization algorithms
Department:Katedra kybernetiky
Supervisor:Ing. Petr Pošík, Ph.D.
Announce as:Diplomová práce, Bakalářská práce, Semestrální projekt
Description:The solution quality of a large-scale optimization problem often depends only on a smaller subset of solution features. Random embedding is a method how to perform the search in a low-dimensional subspace of a high-dimensional search space. The goal of this project is to get acquainted with the principle of random embeddings and implement a test an initial implementation of the algorithm.

(In the future thesis, the topic shall be expanded to perform a larger study of various features: whether the low dimensionality shall be constant or dynamically changing, how often should they change, etc.)
Bibliography:[1] Sanyang M.L., Kabán A. (2016) REMEDA: Random Embedding EDA for Optimising Functions with Intrinsic Dimension. In: Handl J., Hart E., Lewis P., López-Ibáñez M., Ochoa G., Paechter B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science, vol 9921. Springer, Cham
Responsible person: Petr Pošík