Name
Abstract | ||
|---|---|---|
Beautiful paintings can be approximated with remarkably decent quality using a finite list of translucent polygons, each consisting of a finite number of points, and initialized with random color and coordinates. The polygons evolve by repeatedly mutating their color and coordinates until the resulting mutant satisfies some selection criteria for the next generation. In the end, an approximation of the given image is achieved with a good precision given the restriction that the number of polygons and the number of points per polygon are limited. Since its appearance in 2008 under the name "Evolution of Mona Lisa", researchers' interest toward it has decreased despite its initial popularity, which can be partially explained with the lack of a formal publication. In this paper, we describe an efficient natural selection strategy inspired by simulated annealing that, when compared to the existing method, yields better results in every experiment that we conducted. Moreover, this may serve as the first formal introduction to this problem and motivate further research on the topic. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-98446-9_47 | COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2018, PT II |
Keywords | Field | DocType |
Evolution of Mona Lisa, Simulated annealing, Natural selection | Simulated annealing,Data mining,Polygon,Finite set,Computer science,Popularity,Natural selection,Digital image,Theoretical computer science | Conference |
Volume | ISSN | Citations |
11056 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gia Thuan Lam | 1 | 0 | 0.34 |
| Kristiyan Balabanov | 2 | 1 | 1.04 |
| Doina Logofatu | 3 | 17 | 16.74 |
| Costin Badica | 4 | 423 | 70.31 |