Evolutionary Art and Design in the Machine Learning Era
Description
In recent years, there has been a surge in interest in Computational Creativity, with a particular emphasis on adopting Machine Learning (ML) techniques, particularly Deep Learning, in art and design. Generative Artificial Intelligence (AI) models such as Midjourney, Stable Diffusion and DALL-E, to name a few, are currently widespread and can be used to create a wide range of artefacts effortlessly.
While current generative ML models have achieved results, they are not without their flaws. As this tutorial will show, their inclination toward imitation rather than innovation often results in the generation of stock images rather than genuinely creative pieces. These limitations open the door for the application of evolutionary computation techniques, including evolutionary machine learning (EML).
The goals of this tutorial encompass: (i) Present an overview of the current state of the art in Generative AI, distinguishing between data-driven models, such as deep learning models, and non-data-driven approaches, like most evolutionary methods; (ii) Identify the main challenges and opportunities for the application of EML in the fields of Art and Design, which includes the combination of evolutionary approaches with generative ML; (iii) present concrete examples of hybridisation underscoring the uniqueness of their results; (iv) identify open challenges in the field.
In particular, we will address three main pillars for the development of creative and co-creative applications: representation, quality assessment, and user interaction. We will place a particular emphasis on applications and techniques that expand the user's creative possibilities and lead to novel and unforeseen results. We will also reflect upon the gap between academia and the real-world application of evolutionary art approaches and provide examples of how to bridge it, either by incorporating evolutionary techniques into the artistic and design processes or by making them the ultimate goal. Lastly, we will share recent results indicating that EML may outperform mainstream Generative AI techniques for design tasks, where specific requirements exist and form must meet function.
Organizers
Penousal Machado is an Associate Professor at the Department of Informatics of the University of Coimbra in Portugal, the coordinator of the Cognitive and Media Systems, and the scientific director of the Computational Design and Visualization Lab. of CISUC. He is also the president of SPECIES - the Society for the Promotion of Evolutionary Computation in Europe and its Surroundings.
His research interests include Evolutionary Computation, Computational Creativity, Artificial Intelligence, and Information Visualization. He is the author of more than 200 refereed journal and conference papers in these areas, and his peer-reviewed publications have been nominated and awarded multiple times as best papers. His publications gathered over 4000 citations, an h-index of 29, and an i10-index of 99. He was the advisor of 15 PhD theses and 60 MSc theses. Recently, with Wolfgang Banzhaf and Mengjie Zhang, he has edited the"Handbook of Evolutionary Machine Learning" published by Springer.
He is also the chair of several scientific events, including, among the most recent, ICCC 2020, PPSN XV, and EvoStar 2016; member of the Programme Committee and Editorial Board of some of the main conferences and journals in these fields; member of the Steering Committee of EuroGP, EvoMUSART and Evostar.
He has received several scientific awards, including the prestigious EvoStar Award for Outstanding Contribution to Evolutionary Computation in Europe, and the award for Excellence and Merit in Artificial Intelligence granted by the Portuguese Association for Artificial Intelligence.
Penousal Machado has been invited to perform keynote speeches in various domains, from evolutionary computation to visualization and art. His work was featured in the Leonardo journal, Wired magazine and presented in venues such as the National Museum of Contemporary Art (Portugal) and the “Talk to Me” exhibition of the Museum of Modern Art, NY (MoMA).
João Correia is an Assistant Professor at the University of Coimbra, a researcher of the Computational Design and Visualization Lab. and a member of the Evolutionary and Complex Systems (ECOS) of the Centre for Informatics and Systems of the same university. He holds a PhD in Information Science and Technology from the University of Coimbra and an MSc and BS in Informatics Engineering from the same university. His main research interests include Evolutionary Computation, Machine Learning, Adversarial Learning, Computer Vision and Computational Creativity. He is involved in different international program committees of international conferences in the areas of Evolutionary Computation, Artificial Intelligence, Computational Art and Computational Creativity, and he is a reviewer for various conferences and journals for the mentioned areas, namely GECCO and EvoStar, served as remote reviewer for the European Research Council Grants and is an executive board member of SPECIES. He was also the publicity chair and chair of the International Conference of Evolutionary Art Music and Design conference, currently the publicity chair for EvoStar - The Leading European Event on Bio-Inspired Computation and chair of EvoApplications, the International Conference on the Applications of Evolutionary Computation. Furthermore, he has authored and co-authored several articles at the different International Conferences and journals on Artificial Intelligence and Evolutionary Computation. He is involved in national and international projects concerning Evolutionary Computation, Machine Learning, Generative Models, Computational Creativity and Data Science.