English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Handling constraints for combinatorial optimization problems is a\udclassic challenge faced by genetic and evolutionary algorithms. This\udpaper explores a naturally inspired genetic repair process to enforce\udconstraints on evolutionary search. Lolle et al. (2005) controversially\udclaim that the model plant Arabidopsis thaliana appears to repair\udgenetic errors using information inherited from ancestors other than\udthe immediate parents [10] (i.e. non-Mendelian inheritance). We\udadapt this natural template-driven genetic repair process\ud(GeneRepair) to help solve constraint problems. Building upon\udprevious results [6][7][8] this paper explores repair templates that\udoriginate across a range of ancestors, between one and many\udthousands of generations old. The fitness of resulting populations are\udpresented and compared to a benchmark technique using a random\udrepair template [9]. The results show that very ancient (ancestral)\udrepair templates perform best for larger problems, significantly\udoutperforming the benchmark. The impact of background mutation\udrates on solution quality is also explored. Results suggest that\udancestral repair is a good general-purpose constraint handling\udtechnique – helping to explain why this strategy might have evolved\udin nature

Explorations on template-directed genetic repair using ancient ancestors and other templates