Abstract
Evolutionary algorithm analysis is often impeded by the large amounts of intermediate data that is usually discarded and has to be painstakingly reconstructed
for real-world large-scale applications. In the recent past persistent data structures have been developed which offer extremely compact storage with acceptable
runtime penalties. In this work two promising persistent data structures are explored in the context of evolutionary computation with the hope to open the
door to simplified analysis of large-scale evolutionary algorithm runs.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Number of pages | 8 |
| Publication status | Published - 2017 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102011 Formal languages
- 102022 Software development
- 102031 Theoretical computer science
- 603109 Logic
- 202006 Computer hardware
JKU Focus areas
- Computation in Informatics and Mathematics