RT Journal Article
T1 Evaluating genetic algorithms through the approximability hierarchy
A1 Muñoz, Alba
A1 Rubio Díez, Fernando
AB Optimization problems frequently appear in any scientific domain. Most of the times, the corresponding decision problem turns out to be NP-hard, and in these cases genetic algorithms are often used to obtain approximated solutions. However, the difficulty to approximate different NP-hard problems can vary a lot. In this paper, we analyze the usefulness of using genetic algorithms depending on the approximation class the problem belongs to. In particular, we use the standard approximability hierarchy, showing that genetic algorithms are especially useful for the most pessimistic classes of the hierarchy.
PB Elsevier
SN 1877-7503
YR 2021
FD 2021-05-12
LK https://hdl.handle.net/20.500.14352/6785
UL https://hdl.handle.net/20.500.14352/6785
LA eng
NO CRUE-CSIC (Acuerdos Transformativos 2021)
NO Ministerio de Ciencia e Innovación (MICINN)
NO Comunidad de Madrid/FEDER
DS Docta Complutense
RD 6 abr 2025