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This exclusive course is part of the program:
Generative Art and Computational Creativity
Go at your own pace
6 Sessions / 17 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Video Transcripts
English, Spanish; Castilian, Russian, Chinese, Portuguese
Performance Art, Design Architecture, Games, Robotics

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Advanced Generative Art and Computational Creativity

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Go at your own pace
6 Sessions / 17 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Video Transcripts
English, Spanish; Castilian, Russian, Chinese, Portuguese
Performance Art, Design Architecture, Games, Robotics

Not available for purchase in India

Course Description

This course proposes a deepened survey of current practices in generative arts and computational creativity with an emphasis on the formal paradigms and algorithms used for generation. In this advanced class, we study how evolutionary computing, neural networks, and procedural generation can produce novel and valuable artifacts. We survey advances in search-based methods and procedural generation. We look at how to formalize aesthetic measures and learn how creative systems can be evaluated.

We illustrate how these algorithms have been used in numerous examples of past and current productions in visual art, new media, music, poetry, literature, design, architecture, games, moving images, and robot-art. Students get to practice these algorithms first hand and develop new generative pieces through assignments and projects in MAX.

Finally, we discuss the societal and ethical implications of the automation of creative tasks, from the fear of artificial intelligence to the algorithmic bias, and from the most technophobic visions to the most technophilic ideals.


This course is in adaptive mode and is open for enrollment. Learn more about adaptive courses here.

Session 1: Evolutionary Computing and Genetic Algorithms (April 19, 2024)
After a brief introduction to this second part on the topic of generative art and computational creativity, we introduce evolutionary computing and learn how genetic algorithms can be used to evolve new artifacts in visual art and music.
7 lessons
1. Class Introduction
2. Introduction to Evolutionary Theory
3. Genetic Algorithms
4. Breeding Visuals and Forms Interactively
5. Automatic Visual Evolution
6. Genetic Algorithms for Sound and Music
7. Conclusion of the Session
Session 2: Genetic Programming and Evolutionary Ecosystems (April 26, 2024)
In this session, we study how genetic programming is used to breed programs to that generate new artifacts in design and architecture and how evolutionary forces can be used to breed behavior and populations of agents in ecosystemic artworks.
9 lessons
1. Introduction
2. Genetic Programming
3. Evolutionary Design and Architecture
4. Cartesian Genetic Programming
5. Breeding Agents, Breeding Behaviours
6. Evolutionary Ecosystems
7. Musical Evolutionary Ecosystems
8. Conclusion on Evolutionary Ecosystems
9. (R)evolution: Conclusion of Evolutionary Computation
Session 3: Artificial Neural Networks and Deep Learning (May 3, 2024)
This session introduces artificial neural network, and present the perceptron, and multi-layer feed-forward network. Artistic applications of self-organizing maps, neuro-evolution and deep learning are reviewed and discussed.
11 lessons
1. Introduction to Machine Learning
2. Artificial Neural Networks
3. The Perceptron
4. Multilayer Feedforward Networks
5. ANN for Music and Visual Arts
6. ANN in Visual Art
7. Self-Organizing Maps
8. Neuro-Evolution
9. Deep Learning
10. Deep Learning in Art and Music
11. Conclusion: Artificial Neural Networks
Session 4: Search-based Approaches to Creativity (May 10, 2024)
Most creative tasks can be framed as a search problem. This session details advances in procedural content generation for games, and story generation. We also review generative methods used for moving images, dance, choreography, and survey progresses in art making robots.
6 lessons
1. Creativity as a Search
2. Procedural Content Generation in Games
3. Story Generation
4. Generative Video and Film
5. Generative Systems in Dance and Choreography
6. Art Making Robots
Session 5: Evaluation Methods for Computational Creativity (May 17, 2024)
We learned how to develop generative systems for a wide variety of creative tasks, but how good are they? In this session, we cover both informal and formal evaluation methods. We also introduce live coding and discuss the possible bias against computational creativity.
6 lessons
1. Coding as a Practice
2. Evaluation of Creative Systems
3. Formal Evaluation
4. Synthetic Evaluation Methods
5. Empirical Tests for Creativity
6. The Bias Against Computational Creativity
Session 6: Societal and Philosophical Perspectives (May 24, 2024)
To conclude this class, we put generative practices in the more general context of media art. We discuss the fear of automation, and the algorithmic bias. We then present the underlying philosophical debate between technophobia and technophilia and discuss its implications on the relationship between art and science.
4 lessons
1. Ontology of Media Art
2. From the Fear of Artificial Intelligence to the Algorithmic Bias
3. Technophobia and Technophilia
4. Conclusion
Learning Outcomes

Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.

Instructors And Guests
What You Need to Take This Course

• Prerequisite: This is the 2nd course in a 2-course program. Students should take "Generative Art and Computational Creativity" before enrolling in this advanced class.

• Skill Level: Students should have intermediate knowledge of Max/MSP in order to complete assignments.

• Equipment: PC or Mac computer with installation privileges

• Software: MAX 7 license

Additional Information

PLEASE NOTE: Taking part in a Kadenze course as a Premium Member does not affirm that you have been enrolled or accepted for enrollment by the institution offering this course. Credit Eligible students should be prepared to provide additional information and consent to Simon Fraser University terms of service at the start of the course.

In order to receive college credit for these program courses, you must successfully complete and pass all 2 courses in this program. If a student signs up for the Generative Art and Computational Creativity program, it is recommended that these courses are taken sequentially.

*Partial credit will not be awarded for completion of only one course.

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