Painting with Evolutionary Algorithms

Dijkzeul, Danny; Brouwer, Nielis; Pijning, Iris; Koppenhol, Levi; Berg, Daan van den

doi:10.1007/978-3-031-03789-4_4

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…It was shown that GAs outperform the stochastic descent method [52], but may be less efficient than other simple algorithms in the artistic rendering problem. For example, in [5] it was shown that the simulated annealing algorithm could obtain better rendering results in terms of mean squared error when compared to the HillClimber and plant propagation evolutionary algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Throughout history, technology has greatly expanded the creative and professional possibilities of artists, providing them with new and more powerful tools and enabling them to create novel artistic styles and art forms. Computer graphics, and in particular nonphotorealistic rendering (NPR), have a great influence on the development of contemporary art and are often used to create web content [1][2][3][4][5], for robotic painting [6][7][8], as a tool for creating comics [9], and, of course, for imitating artistic paintings [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Neural Style Transfer and Gradient-Based Algorithms in Brushstroke Rendering Tasks

Karimov,

Kopets,

Shpilevaya

et al. 2023

Mathematics

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Non-photorealistic rendering (NPR) with explicit brushstroke representation is essential for both high-grade imitating of artistic paintings and generating commands for artistically skilled robots. Some algorithms for this purpose have been recently developed based on simple heuristics, e.g., using an image gradient for driving brushstroke orientation. The notable drawback of such algorithms is the impossibility of automatic learning to reproduce an individual artist’s style. In contrast, popular neural style transfer (NST) algorithms are aimed at this goal by their design. The question arises: how good is the performance of neural style transfer methods in comparison with the heuristic approaches? To answer this question, we develop a novel method for experimentally quantifying brushstroke rendering algorithms. This method is based on correlation analysis applied to histograms of six brushstroke parameters: length, orientation, straightness, number of neighboring brushstrokes (NBS-NB), number of brushstrokes with similar orientations in the neighborhood (NBS-SO), and orientation standard deviation in the neighborhood (OSD-NB). This method numerically captures similarities and differences in the distributions of brushstroke parameters and allows comparison of two NPR algorithms. We perform an investigation of the brushstrokes generated by the heuristic algorithm and the NST algorithm. The results imply that while the neural style transfer and the heuristic algorithms give rather different parameter histograms, their capabilities of mimicking individual artistic manner are limited comparably. A direct comparison of NBS-NB histograms of brushstrokes generated by these algorithms and of brushstrokes extracted from a real painting confirms this finding.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing Neural Style Transfer and Gradient-Based Algorithms in Brushstroke Rendering Tasks

Karimov,

Kopets,

Shpilevaya

et al. 2023

Mathematics

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“…Being population-based, it revolves around the idea that fitter individuals produce more offspring with fewer mutations, while unfitter individuals produce fewer offspring with more mutations, all in an effort to balance the forces of exploration and exploitation when traversing the combinatorial state space. Since its inception, many implementations, variations and applications have been studied and published [16] One variation in particular already deals with TSP [51]. Although that implementation does abide by the core principle of PPA, it designates constants to individuals for offspring and mutation rates, whereas 'classic' PPA relates these parameters through a smooth sigmoidal 𝑡𝑎𝑛ℎ-function (Eq.…”

Section: Plant Propagation Algorithmmentioning

confidence: 99%

Exactly characterizable parameter settings in a crossoverless evolutionary algorithm

Koppenhol

Brouwer

Dijkzeul

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…By normalizing the population's fitness before creating offspring, there are always relatively fitter and unfitter individuals, which as a whole balances the efforts of exploration and exploitation. Although the algorithm is relatively new, it has seen numerous applications in diverse areas like digital art [11,29] and generating hard-to-solve problem instances for an NP-complete problem [36,37]. Some more practical implementations include the university timetabling problem [18], nurse rostering [20] and the traveling salesman problem [35].…”

Section: Introductionmentioning

confidence: 99%