Accelerated pattern search with variable solution size for simultaneous instance selection and generation
Hoang Lam Le, Ferrante Neri, Dario Landa-Silva, Isaac Triguero · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is often performed in separated stages, combining instance selection (IS) first, and sub-sequently instance generation (IG). This paper investigates a fast optimisation approach for IR considering the two stages at once. This approach, namely Accelerated Pattern Search with Variable Solution Size (APS-VSS), is characterised by a variable solution size, an accelerated objective function computation, and a single-point memetic structure designed for IG.