Relevant content for the tag: genetic-programming


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

S. DiPaola, G. McCaig

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

S. Salevati, S. DiPaola

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

S. Salevati, S. DiPaola

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

S. DiPaola

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

S. DiPaola, G. McCaig, K. Carlson, S. Salevati, N. Sorenson

Sydney, Australia

How Did Humans Become So Creative? A Computational Approach
Conference Proceedings: International Conference on Computational Creativity, 2012
pp 203-211

L. Gabora, S. DiPaola

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

N. Sorenson, P. Pasquier, S. DiPaola


Cartesian Genetic Programming, Creativity and Art
Book Chapter: Cartesian Genetic Programming, 2010
Editor: Miller J, 22 pages, in press

S. DiPaola, N. Sorenson



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.