The 16th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMusArt) is a multidisciplinary conference that brings together researchers working on the application of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Cellular Automata, Artificial Life (Alife), Generative AI, Foundation Models, and other Artificial Intelligence (AI) techniques in creative and artistic fields such as Visual Art, Music, Architecture, Video, Digital Games, Poetry, Design, and Interactive Media. The conference provides a forum for presenting and discussing novel research, artistic practices, systems, and applications that explore computational creativity, human-AI co-creation, and emerging creative technologies. Submissions must be at most 14 pages long, excluding references, in Springer Lecture Notes in Computer Science (LNCS) format. Each submission must be anonymised for a double-blind review process.

Accepted papers will be presented orally or as posters at the event and included in the EvoMusArt proceedings published by Springer Nature in a dedicated volume of the LNCS series.

Submissions should address the use of AI techniques (e.g. Evolutionary Computation, Artificial Neural Networks, Alife, Machine Learning (ML), Deep Learning, and Swarm Intelligence) in creative and artistic domains, including art, music, design, architecture, and related fields. Topics of interest span generation, automation, computer-aided creativity and creativity support tools, and theoretical aspects of computational creativity, including but not limited to:
— Systems that create drawings, images, animations, videos, sculptures, poetry, text, designs, webpages, buildings, virtual environments, and other creative artefacts;
— Systems that create musical pieces, sounds, instruments, voices, sound effects, soundscapes, and multimodal artistic experiences;
— Systems that create artefacts such as game content, architecture, furniture, industrial products, and digital experiences based on aesthetic and functional criteria;
— Generative AI, foundation models, large language models (LLMs), multimodal models, and diffusion-based creative systems;
— Computational creativity in digital games, interactive storytelling, and narrative generation;
— Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR) for artistic and creative applications, including immersive art installations and interactive creative environments;
— Robotic-based Evolutionary Art and Music;
— Embodied AI, tangible and wearable interfaces, and smart environments for creative applications;
— Digital fabrication, 3D printing, and computational approaches to physical artefact creation;
— Other artificial, generative, evolutionary, or biologically inspired techniques in the fields of Computer Music, Computer Art, Design, and Digital Culture;
— Techniques for automatic fitness assignment;
— AI agents and autonomous creative systems;
— Systems in which analysis or interpretation of artworks is combined with AI techniques to generate novel artefacts;
— Systems that use AI for the analysis, understanding, restoration, preservation, or curation of artistic and cultural heritage resources;
— Human-AI co-creation, mixed-initiative systems, intelligent interfaces, and AI-based creativity support tools;
— New ways of integrating users and audiences into the evolutionary, generative, or creative cycle;
— User-centred evaluation of creative AI systems and creative experiences;
— Analysis and evaluation of the artistic potential of biologically inspired and AI-based creative systems, their creative processes, and resulting artefacts;
— Collaborative, distributed, and multi-user creative systems, networked art environments, and collective computational creativity;
— Contextualisation of Creative AI in cultural, economic, social, political, legal, ethical, ecological, and sustainability-related discourse.
— Computational Aesthetics, Experimental Aesthetics, Emotional Response, Engagement, Surprise, Novelty, and Meaning;
— Representation techniques and creative knowledge representations;
— Explainability, transparency, authorship, ownership, and ethics in Creative AI;
— Surveys of the current state of the art, identification of strengths and weaknesses, comparative analyses and taxonomies;
— Validation and evaluation methodologies for creative systems and generated artefacts;
— Studies on the applicability of these techniques to related domains;
— New models designed to promote creativity through evolutionary computation, Alife, ML, and hybrid approaches.

For more information on the submission process and topics, visit:
https://www.evostar.org/2027/evomusart/

Flyer: http://www.evostar.org/2027/flyers/evomusart

Papers published in previous editions of EvoMusArt: https://evomusart-index.dei.uc.pt

The EvoMUSART 2027 organisers
Sérgio M. Rebelo
Patrick Donnelly
Nereida Rodríguez-Fernández (publication chair)