High dimensional optimisation is a challenge for most of the available search methods. Resolving global and constrained task seems to be even harder and exploration of tasks with unknown solutions can be seen very rare in the literature and requires more research efforts. This article analyses optimisation of high dimensional global, including constrained, tasks with unknown solutions. Reviewed and analysed are experimental results precision, possibilities for trapping in local sub-optima and adaptation to unknown search spaces.
|Title of host publication||Large-Scale Scientific Computing|
|Subtitle of host publication||LSSC 2019. Lecture Notes in Computer Science|
|Editors||Ivan Lirkov, Svetozar Margenov|
|Place of Publication||Cham|
|Number of pages||6|
|Publication status||Published - 13 Jan 2020|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|