Evolutionary algorithms have been used in various ways to create or guide the creation of digital art. In this tutorial we present techniques from the thriving field of biologically inspired art. We show how evolutionary computation methods can be used to enhance artistic creativity and lead to software systems that help users to create artistic work.
We start by providing a general introduction into the use of evolutionary computation methods for digital art and highlight different application areas. This covers different evolutionary algorithms including genetic programming for the creation of artistic images. Afterwards, we discuss evolutionary algorithms to create artistic artwork in the context of image transition and animation. We show how the connection between evolutionary computation methods and a professional artistic approach finds application in digital animation and new media art, and discuss the different steps of involving evolutionary algorithms for image transition into the creation of paintings. Afterwards, we give an overview on the use of aesthetic features to evaluate digital art. The feature-based approach complements the existing evaluation through human judgments/analysis and allows to judge digital art in a quantitative way. Finally, we outline directions for future research and discuss some open problems.
• Aneta Neumann graduated in Computer Science from the Christian-Albrechts-University of Kiel, Germany and received her PhD from the University of Adelaide, Australia. She presented invited talks at UCL London, Goldsmiths, University of London, University of Nottingham, University of Sheffield, Hasso Plattner Institut University of Potsdam, Sorbonne University, University of Melbourne, University of Sydney in 2016-19. She received the ACM Women scholarship, sponsored by Google, Microsoft, and Oracle, the Hans-Juergen and Marianna Ohff Research Grant in 2018, and the Best Paper Nomination at GECCO 2019 in the track “Genetic Algorithms”. Her main research interest focuses on bio-inspired computation, evolutionary diversity optimisation, submodular functions, and optimisation under uncertainty in practice. She serves as the co-chair of the Real-World Applications track at GECCO 2021.
• Frank Neumann received his diploma and Ph.D. from the Christian-Albrechts-University of Kiel in 2002 and 2006, respectively. Currently, he is a full Professor and leader of the Optimisation and Logistics Group at the School of Computer Science, The University of Adelaide, Australia. Frank has been the general chair of the ACM Genetic and Evolutionary Computation Conference (GECCO) 2016 and has given several tutorials at GECCO and PPSN over the last 10 years. He co-organised ACM Foundations of Genetic Algorithms (FOGA) 2013 and is an author of the textbook "Bioinspired Computation in Combinatorial Optimization - Algorithms and Their Computational Complexity" published by Springer. Currently, he is an Associate Editor of the journals "Evolutionary Computation” and "ACM Transactions on Evolutionary Learning and Optimization". In his work, he considers algorithmic approaches in particular for combinatorial and multi-objective optimization problems and focuses on theoretical aspects of evolutionary computation as well as high impact applications in the areas of creativity, renewable energy, logistics, and mining.