Relevant content for the tag: genetic-programming
Publications
Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter
Conference Proceedings: Electronic Visualisation and the Arts, British Computer Society, 2016
8 pages
London, UK
A Creative Artificial Intelligence System to Investigate User Experience, Affect, Emotion and Creativity
Conference Proceedings: Electronic Visualisation and the Arts, 2015
8 pages
London, UK
British Computer Society
Enhancing Viewer's Emotional Connections to the Traditional Art Creative Process via an AI Interactive System
Conference Proceedings: Electronic Visualisation and the Arts, Florence, 2015
8 pages
Florence, Italy
Using a Contextual Focus Model for an Automatic Creativity Algorithm to Generate Art Work
Journal Article: Procedia Computer Science. Special Issue: Bio Inspired Cognitive Architectures, 2014
Vol 41, pp. 212-219
Adaptation of an Autonomous Creative Evolutionary System for Real-World Design Application Based on Creative Cognition
Conference Proceedings: Fourth International Conference on Computational Creativity, 2013
pp 40-47
Sydney, Australia
How Did Humans Become So Creative? A Computational Approach
Conference Proceedings: International Conference on Computational Creativity, 2012
pp 203-211
Dublin, Ireland
A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels
Journal Article: Transactions on Computational Intelligence and AI in Games, 2011
Vol 3, No 3, pp 229-244
IEEE
Cartesian Genetic Programming, Creativity and Art
Book Chapter: Cartesian Genetic Programming, 2010
Editor: Miller J, 22 pages, in press
Springer
Research
Evolving Creative Computer Programs w/Genetic Programming
This research uses creative evolutionary systems to explore computer creativity for various applications (in our first pass – evolving a family of abstract portrait painter programs). We use relatively new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) first developed by Julian Miller .
GenFace - Exploring FaceSpace with Genetic Algorithms
Imagine an -dimensional space describing every conceivable humanoid face, where each dimension represents a different facial characteristic. Within this continuous space, it would be possible to traverse a path from any face to any other face, morphing through locally similar faces along that path. We will describe and demonstrate a development system we have created to explore what it means to ‘surf’through face space. We will present our investigation of the relationships between facial types and how this understanding can be used to create new communication and expression systems.