Detail of the student project

List
Topic:Random embeddings for black-box optimization algorithms
Department:Katedra kybernetiky
Supervisor:Ing. Petr Pošík Ph.D.
Announce as:DP,BP
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 design a method of using random embeddings in black-box optimization algorithms (local search, evolutionary algorithms): whether the embedding shall be static or dynamic, whether the low dimensionality shall be constant or dynamically changing, how often should they change, etc. The resulting method shall then be compared at least to the chosen optimization algorithm without random embedding.
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
Date:10.05.2019
Responsible person: Petr Pošík