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Evolutionary Art

Explore the creation of captivating digital art using Genetic Algorithms with this project. The objective is to develop aesthetically pleasing and evolvable artwork through a Graphical User Interface implemented in Java. Experimentation includes parameters like shape type, color palette, symmetry, and more. The interface design is user-friendly, but improvements like tournament style selection are suggested. Future work involves implementing other evolutionary algorithms and enhancing visual effects. Special thanks to Ben, Gusz, Bart, and Andrew for their contributions.

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Evolutionary Art

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  1. Evolutionary Art (What we did on our holidays) David Broadhurst Dan Costelloe Lynne Jones Pantelis Nasikas Joanne Walker

  2. Introduction • Project aims • Develop some “nice” art • Use Genetic Algorithms to evolve art • Analyse the human-computer interface

  3. Our Tools • Graphical User Interface written in java • Genetic Algorithm Engine • Evolvable .gif images

  4. Objectives • Primary objective: “nice” art • Secondary objective: modify GA engine

  5. Art Requirements • Needs to be simple yet attractive • Evolvable – through parameters • Written in simple java for easy integration

  6. Art Used Kaleidoscope applet • Simple shapes & bright colours • Use of reflections and symmetry add interest • Animation gives extra dimension • Small simple java applet (Demo)

  7. Chromosome Design • Existing code used integer array • Our parameters: • Shape type (line / rectangle / circle / mix) • Colour of shapes (10 x 255 colour palette) • Background colour • Symmetry style (horizontal / vertical / diagonal / mix)

  8. Experimentation • Considered mutation rate and crossover type • Attempted to evolve a population of solutions without circles • Recorded speed of convergence

  9. Mutation • Guassian mutation • Probability varied between 0 and 1 • As rate increased more generations were required • Convergence of other parameters increased as mutation rate decreased

  10. Crossover • Considered three types: • Single point • Random N point • Uniform • Similar results obtained for each

  11. Demo

  12. Human Computer Interface • Current layout is easy to use • Can be time consuming after a few generations • Tournament style selection may be an improvement

  13. Conclusions • Future work • Implement other evolutionary algorithms • Addition of visual effects on the animations • Revision of Human Machine Interface

  14. Thanks to…. • Ben • Gusz • Bart (dude) • Andrew

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