Origins of Interactive EvolutionaryComputation I. C. Parmee GECCO Workshop, 2002

1. Origins of Interactive EvolutionaryComputationI. C. ParmeeGECCO Workshop, 2002

2. Initial Interactive Evolutionary Systems addressed Artistic Image Creation, Music, Fashion etc where user provided aesthetic judgement when presented with images or sound bytes History 1986 - Richard Dawkin’s biomorphs (Blind Watchmaker) 1991 - Sims 1991 - Todd & Latham’s Mutator 1993 - Tatsuo Unemi’s SBART 1999 - Whitbrock & Niel-O’Reilly’s IIGA1 1999? - Moore’s GA Music 2000 - Kim & Cho’s Fashion Design

3. Dawkin’s Biomorphs • Recursive tree-branching drawing rule to generate diverse structures • Branching rule analogous to cell-splitting and embryonic development through influence of genes. • Nine genes introduce differing influences on branching rule e.g. depth of recursion, angle of branching etc - population then mutated from initial parent.

4. Human selects most pleasing tree structure to be parent of next mutated population • Insect-like creatures and other recogniseable shapes evolved via the nine controlling genes, the recursive branching rule and human interaction in the form of subjective evaluation.

5. Karl Sims • Computer Graphics, 25(4), July 1991, pp. 319-328.(ACM SIGGRAPH '91 Conference Proceedings, Las Vegas, Nevada, July 1991.) • 3D plant structures grown using a set of ``genetic'' parameters. • Parameters describing fractal limits, branching factors, scaling, stochastic contributions, etc., are used to generate 3-dimensional tree structures consisting of connected segments. • Rules use 21 genetic parameters and hierarchy location of each segment in tree to determine : • how fast segment grows, • when it generates new buds, and in which directions.

6. Desirable tree structure evolved using stochastic mutation, crossover and interactive user selection. • Phenotype can be saved for further manipulation. • e.g. Solid polygonal branches can be generated with connected cylinders and cone shapes • leaves can be generated by connecting sets of peripheral nodes with polygonal surfaces. • Shading parameters, color, and bump textures can be assigned to make bark and leaf surfaces. • Figure 3 Forest of ``evolved'' plants.

7. Procedural information included in genotype in addition to parameter data, • Symbolic lisp expressions used as genotypes so that procedural and data elements of genotype not be restricted to a specific structure or size. • Set of lisp functions and set of argument generators used to create arbitrary expressions which can be mutated, evolved, and evaluated to generate phenotypes. • Generation of textures by mutating symbolic expressions. Equations that calculate a color for each pixel coordinate (x,y) are evolved using a function set containing standard common lisp functions, vector transformations, procedural noise generators, and image processing operations • Each function takes a specified number of arguments and calculates and returns an image of scalar (b/w) or vector (color) values. • Population of simple random expressions displayed in grid for interactive selection.

8. Expressions of images selected by user are reproduced with mutations for each new generation such that more and more complex expressions and more perceptually successful images can evolve (figures 9 to 13). • Exhibitions: • Genetic Images - 1993 media installation - visitors interactively "evolve" abstract still images. Computer generates and displays 16 images on arc of screens. Visitors stand on sensors in front of most aesthetically pleasing images to select which ones will survive and reproduce to make the next generation. Exhibited at the Centre Georges Pompidou, Paris, Ars Electronica, Linz, and the Interactive Media Festival, Los Angeles. • Galápagos - interactive media installation that allows visitors to "evolve" 3D animated forms. Installed at the ICC in Tokyo from 1997 to 2000, and was exhibited at the DeCordova Museum in Lincoln, Mass. as part of Make Your Move: Interactive Computer Art and the Boston Cyberarts Festival 1999.

9. Mutator Interactive creation of artistic images Process: 1) Establishes structure from set of primitives (ribs) to produce horns (collection of ribs); 2) Generates gene vector containing transformation rules e.g. grow, stack, bend, twist etc); 3) Presents 8 mutated forms (plus parent) to the user; 4) User selects favourite which is archived and used as parent for next stage of mutation; 5) All 8 mutated forms are ranked - this ranking utilised to generate factors relating to degree of each type of transformation in next stage of mutation; 6) Two parents can be selected to create 7 children via a mixing algorithm that splices parts of each image.

10. Mutator (cont) PC based version with easy interface where all mutation and crossover aspects are hidden from the user has been successfully used by five year old children during Public exhibitions. System has resulted in a number of commercial products: computer games, music CD covers, clothing patterns and cinematic special effects. Also been developed to generate Music through: 1) The mixing of instruments - variables include relative volume of each instrument and start and end times; 2) Musical textures - algorithmic generation of notes - genes control average length, gaps between them, pitch range, pitch differnce between adjacent notes. More global genes control degree of rhythm and pitch synchronisation between lines.

11. Interface different as pieces have to be heard and compared sequentially. User plays, listens and makes judgement. Older mutations can be included in this comparison. Practical Limitations 1) Programming difficulties integrating Mutator with other applications; 2) Difficulty in building models - Graphical users trained to create finished results not parameterised models.

12. SBART - Tatsuo Unemi • Comprehensive evolutionary art program freely available at www.intlab.soka.ac.jp/~unemi/sbart. • Genetic Programming based - SBART assigns arithmetic operators to non-terminal nodes of GP tree and constant and x,y variables to terminals. X,y denotes pixel whereas value of function denotes colour. • Graphic movie can be created by introducing time variable. • User can evolve own images or can load a peviously saved session. • Populations can be saved and re-loaded and images can be dragged from one window to another thereby introducing new species for cross-breeding • Possible to edit GP tree via cut and paste thereby modifying images.

13. IIGA- International Interactive Genetic Art - Whitlock & Niel-O’Reilly • Very similar to Sim’s and Unemi’s work in that Genetic Programming utilised to manipulate set of primitives where x and y denote pixel co-ordinates and various functions evaluated at x,y denote pixel colour. • Web-based (www.geneticart.org) user’s aesthetic preferences applied to nine generated images which are rated between 1 and 10. • Preferences collected from ten users before new generation is produced via crossover and mutation.

14. GA Music - J. H. Moore • Web-based (www-ks.rus.uni -stuttgart.de/people/schulz /fmusic/gamusic.html) • GA manipulates 128 bit string controlling pitch and duration of max thirty notes melodies. • Pallete of 12 melodies provided - user rates in terms of poor, average, good. • Control provided over crossover and mutation rates • Early generations produce pure cacaphony that can be painful but melodies do emerge.

15. Fashion Design - H-S Kim, S-B Cho • Database of partial design elements - each design stored as 3D model • Population randomly created with each individual composed from combination of parts. • Individuals displayed on screen - user assigns fitness values to each one • Fitness proportionate reproduction followed by crossover and mutation o produce next generation to be displayed…..

16. Domain-specific knowledge included by reclassifying general detail factors in three parts: • Neck and body - 34 models (6 bits) includes neckline, collar and body shape; • Arm and sleeve - 12 models (4 bits) including armless design; • Skirt and waistline - 9 models (4 bits). • Extra 3 bits in chromosome encodes colour- 8 alternatives. • To test system 500 sample designs first generated - 3 subjects requested to rate designs in terms of coolness and splendour (5 ranks -2 to +2) • Average scores provide 10 most cool and 10 most splendid. • 10 subjects then requested to find cool and splendid looking designs using the system with searching limited to 10 generations. • Each subject selects best one from final population and compares it to t

17. those selected in initial test set with scores between -3 and + 3, statistical analysis then applied • Subjects average score in the confidence interval 95 - 99% is 2.17 for cool design and 1.74 for splendid design from which authors conclude that the system shows significant promise for non-professional users.

18. Where is this work going? • Is there anywhere for it to go? • What are the limitations? • What is the potential? • Can algorithms be improved? • Can processes be improved? • Any representation issues?