Genetic algorithms (GA) for adaptable design
Genetic algorithms (GA) for adaptable design
This thesis discusses the process of implementing and testing of a creative web element design system using a combination of genetic algorithms and K-nearest neighbor classification. Classification will be used as a learning mechanism to give the system the ability to absorb quality measures in regards to visual aesthetics, and utilize this knowledge to evaluate generated designs. In addition, some basic concepts regarding web design will be covered, along with an introduction to artificial intelligence-based design. The results of the implementation will be discussed and compared to related state-of-the-art systems.
web design, Genetic algorithm, K-nearest neighbor, VDP::Technology: 500::Information and communication technology: 550, SHO6264, VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550, classification
web design, Genetic algorithm, K-nearest neighbor, VDP::Technology: 500::Information and communication technology: 550, SHO6264, VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550, classification
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!