Visual art inspired by the collective feeding behavior of sand-bubbler crabs

Sand--bubblers are crabs of the genera Dotilla and Scopimera which are known to produce remarkable patterns and structures at tropical beaches. From these pattern-making abilities, we may draw inspiration for digital visual art. A simple mathematical model is proposed and an algorithm is designed that may create such sand-bubbler patterns artificially. In addition, design parameters to modify the patterns are identified and analyzed by computational aesthetic measures. Finally, an extension of the algorithm is discussed that may enable controlling and guiding generative evolution of the art-making process

Automatic sound synthesis using the fly algorithm

Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies’representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language

20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

The proceedings contain 57 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Minimization of systemic risk for directed network using genetic algorithm; pricing rainfall based futures using genetic programming; dynamic portfolio optimization in ultra-high frequency environment; integration of reaction kinetics theory and gene expression programming to infer reaction mechanism; improving the reproducibility of genetic association results using genotype resampling methods; characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data; application to blood glucose forecasting; genetic programming representations for multi dimensional feature learning in biomedical classification; meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing; analysis of average communicability in complex networks; configuring dynamic heterogeneous wireless communications networks using a customised genetic algorithm; multi-objective evolutionary algorithms for influence maximization in social networks; Lamarckian and lifelong memetic search in agent-based computing; two-phase strategy managing insensitivity in global optimization; avenues for the use of cellular automata in image segmentation; localization on hubs and delocalized diffusion; hybrid multi-ensemble scheduling; driving in TORCS using modular fuzzy controllers; automated game balancing in ms pacman and starcraft using evolutionary algorithms; evolving game specific UCB alternatives for general video game playing; analysis of vanilla rolling horizon evolution parameters in general video game playing and evolutionary art using the fly algorithm