In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.
|Name||Lecture Notes in Computer Science|
|Conference||5th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and design|
|Abbreviated title||EvoMUSART 2016|
|Period||30/03/16 → 1/04/16|
- novelty search
- multi-objective optimisation