Evolving Line Drawings

Ellie BakerMargo Seltzer

Aiken Computation Laboratory

Harvard University

33 Oxford Street

Cambridge, MA 02139

e-mail: ellie@das.harvard.edu

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AbstractThis paper explores the application of interactiv

genetic algorithms to the creation of line drawings. Whave built a system that mates or mutates drawinselected by the user to create a new generation of draings. The initial population from which the user makeselections may be generated randomly or input manally. The process of selection and procreation is repeamany times to evolve a drawing. A wide variety of complex sketches with highlighting and shading can bevolved from very simple drawings. This technique hapotential for augmenting and enhancing the powertraditional computer-aided drawing tools, and foexpanding the repertoire of the computer-assisted arti

Keywords: Genetic Algorithm, Interactive Evolu-tion, Interactive Graphics.

1 IntroductionInteractive fitness evaluation for genetic algorithm

was introduced by zoologist Richard Dawkins in hibook, “The Blind Watchmaker.” The book describescomputer program for evolving images of creatureDawkins calls “biomorphs” [Daw87]. Each biomorph isproduced from a compact genetic code that specifiescreature’s particular characteristics. Contained in thcode is the essential information necessary to creatbit-mapped image of the creature on the computscreen. Using a technique calledinteractive evolution,the computer and user collaborate to produce compand varied insect-like creatures. With interactive evolution, the user selects the most aesthetically pleasing bmorph from a set of creatures displayed on the screeBy randomly mutating the genetic code of the selectebiomorph, the computer creates a new generation of bmorphs to display. The new generation is again sujected to the aesthetic selection criteria of the user

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produce the following generation. This cycle continueuntil the user “evolves” a pleasing creature.

Dawkins’ idea has spawned several successful appcations of interactive evolution to other image desigand construction problems ([Sim91], [CJ91], [TL92][Sth91], [BS93], [Ba93]). This paper presents an interative evolution system for creating line drawings. Thgenetic representation and definition of the genetic opators are substantially different from previous interactivevolution systems, and it produces correspondingunique results. The user interface is similar to other sytems (especially Sims’ [Sim91] and Dawkins’ [Daw87])An initial population of drawings, either generated randomly by the computer or input by the user, is displayeon the screen. From the displayed set the user selectsdrawing for mutation or two drawings for mating. Themating and/or mutation operations are applied to thselected drawings to produce a new set of progeny draings that supply the input for the next round of useselection. This process is repeated multiple times“evolve” a drawing of interest to the user. Evolved drawings may be saved and later recalled for mating wiother evolved drawings.

2 Interactive EvolutionInteractive evolution provides a powerful new tech

nique for enabling human-computer collaboration. Itpotentially applicable to a wide variety of search problems, provided the candidate solutions can be producquickly by a computer and evaluated quickly and easiby a human. Since humans are often very good at pcessing and assessing pictures quickly, interactive evotion is particularly well suited to search problems whoscandidate solutions can be represented visually.

Traditionally a genetic algorithm (GA) requires thespecification of survival fitness criteria to be evaluateby the computer [Gol89]. This is typically one of themost difficult tasks in designing a GA. With interactive

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evolution the user performs this step, applying whatevcomplicated measure of fitness is desired. Unfortunateincluding a human evaluator also severely weakens tGA because the human’s speed and patience beconew limiting factors, restricting the population size anpossible number of generations. Despite this drawbainteractive evolution has been used to produce somastounding results that could not have been achieveasily by any other known method [Sim91], [TL92].

The beauty of interactive evolution is that the useneed onlyapply personal fitness criteria, not state oeven understand them. This feature of interactive evotion is used very effectively in a system by Caldwell anJohnston for allowing a crime victim to produce a faciacomposite of a criminal suspect [CJ91]. This systetakes advantage of the remarkable human ability to reognize faces. A database of face parts (e.g., eyes, nomouths) is used to construct candidate faces, which athen rated by a human operator for their degree of likness to the suspect. Based on these ratings, the facesrecombined and mutated to produce a new generationfaces. This rating and procreation process is repeauntil a likeness to the suspect is achieved. Caldwell aJohnston state that, while “humans have excellent facrecognition ability,” they “have great difficulty in recall-ing facial characteristics with sufficient detail to providan accurate composite” [CJ91]. This is a perfect example of the ability to apply a complex fitness test withouconsciously understanding it. Since computers have htorically performed poorly at face recognition (compared to humans), the system employs a particulasuitable division of labor between human and comput

Sims’ system uses interactive evolution to creabeautiful, abstract color images [Sim91]. The genetcode for an image is a Lisp expression representingsequence of image-processing functions (i.e., functiothat take as input a set of pixel values and associacoordinates, and produce new pixel values as outpuSims uses a fixed set of image-processing primitiveand uses interactive evolution to evolve increasingcomplex functions. The search space consists of all Liexpressions that can be constructed from the primitiveThe mutation and mating operators restructure or moify the Lisp expressions and make random paramechanges. Sims also evolves plant forms by applyininteractive evolution to L-Systems, grammars thadescribe biological models of plant growth [Sim91[LP89].

Other interactive evolution systems of note includeDawkins’, which uses a recursive genetic structure thproduces varied, but highly characteristic insect-likforms [Daw87]; Todd and Latham’s, which uses constructive solid geometry techniques to evolve “virtua

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sculptures” [TL92]; Smith’s, which uses a Fourierseries-based representation to produce bug-like curvline forms [Sth91]; and Oppenheimer’s, which producelife-like 3D tree forms using a recursive fractal represetation similar to Dawkins’ [Opp89].

These systems use a variety of genetic representions to explore both infinite and large finite spaceEach system relies on the ability to represent candidasolutions visually and on the human ability to evaluathese solutions quickly and in parallel. The evaluatiocriteria used are difficult-to-articulate personalizeassessments of such poorly defined characteristics“interesting,” “aesthetically beautiful,” “good likeness,”or “life-like.” These are terms whose definitions mavary drastically from person to person or even chanfrom moment to moment in the same person. It is thtype of search problem for which interactive evolutioprovides an exciting new tool.

To date only a handful of interactive evolution applications have been built. These few applications hashown interactive evolution to be an interesting and usful tool, but there is still untapped potential in manyother areas as well. One goal of this research is to addcurrent understanding of interactive evolution by applying it in a new domain. In doing so we hope to gain fresinsight into both its power and its limitations.

At the application level, the goal is to build a newkind of computer-aided drawing tool. Traditional toolsuse a compact, object-oriented drawing representatthat makes it easy for a user to apply many operationsthe drawing. Unlike a bit-mapped image, this high-leverepresentation allows the user easy manipulation of invidual drawing features, such as the ability to deletemodify individual lines, points, or other objects. However, creation of drawings in this format requires vergood eye-hand coordination, and, for anything eveslightly complex, a great deal of effort and tediumUsing these tools to create drawings beyond a certalevel of complexity can be all but impossible, especiallfor a non-artist. The work presented here is intendeddemonstrate the potential for using interactive evolutioto augment and enhance the power of traditional computer-aided drawing tools and to expand the repertoirethe computer-assisted artist.

Section 3 describes the Drawing Evolver and showhow interactive evolution can be used to create compldrawings in a high-level format. Section 4 discusses oexperience using the Drawing Evolver.

3 Drawing EvolverThe Drawing Evolver is an interactive evolution sys

tem written in the C programming language that rununder X Windows on a Unix workstation. Its basic com

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ponents are: a structured representation for drawingsmeans of producing an initial population of drawingsmating and mutation operators; and an operator that pduces a drawing from its corresponding genetic code.

3.1 Drawing RepresentationIn the language of biologists, the genetic constitutio

of an organism is referred to as itsgenotype, and thephysical manifestation of the organism as itsphenotype.In the Drawing Evolver, the organisms are drawingwhose appearance (or phenotype) is determined by thgenetic code (or genotype). The core of the system is tstructure of the genotypes used to represent drawingsgenotype consists of an ordered set of “strokes,” wheeach stroke is represented by an ordered set of poiand a method for connecting them (e.g., a spline curor straight lines). A stroke may be loosely thought of aa mark made by a pencil without lifting it off the pageThe stroke specification also includes other per-stroparameters. For example, a symmetry type (horizontvertical, both, or neither) is given for each stroke. A seof symmetric marks are encoded as a single mark withstated symmetry type. In this case several disconnecmarks are still referred to as one stroke. The numberstrokes in a drawing, as well as the number of points instroke, can vary, resulting in genotypes of varyinlength, and drawings of varying complexity.

In theory, this representation provides for the possbility of any line drawing contained within the drawingframe. The number of possible phenotypes is very largbut finite. Since there are only two possible values (onoroff) for every pixel in the drawing frame, and we use250 by 250 pixel square frame, there are a total

distinct phenotypes (over 18000 orders of magnitude larger than Caldwell and Johnston’s search spaof 34 billion possible composites [CJ91]). Figure 1 illus

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Figure 1: A simple example drawing and its corre-sponding genotype (below it). Although the drawinghas three separate marks, it consists of only twostrokes. The jagged stroke has the property of being“horizontally symmetric,” so it consists of two separatemarks mirroring each other across a vertical axis.

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trates a simple drawing and its corresponding genotypTable 1 specifies how drawing genotypes are interprete

To mutate a drawing, randomly chosen strokesstroke points are moved, deleted, or added, and stroparameters are randomly modified. When two drawinare mated, a randomly chosen subset of strokes froeach of the parent drawings are combined to form a nedrawing. These operations are described in more dein Section 3.3 and Section 3.4.

3.2 Operation ModesThe system has two modes of operation that diff

with respect to how an initial population of drawings icreated. Inrandom mode, the computer creates an initiapopulation of randomly generated drawings. Inuser-

INTERPRETING A DRA WING GENOTYPE

Each stroke is represented by two consecutive lines of teone for the stroke parameters, and one giving an ordered sethe x,y coordinates of the stroke points (i.e., x1 y1 x2 y2 xy3). The stroke parameters are as follows:

• ID (optional)

An identifier for the stroke.

• STROKE TYPE

O = Open (first and last points are not connected)

S = Space Enclosing (first and last points are connected

G = Glued to Next Stroke (last point is connected to firstpoint of next stroke)

• SYMMETRY TYPE

N = No Symmetry

V = Vertical Symmetry

H = Horizontal Symmetry

A = Vertical and Horizontal Symmetry

• POINT CONNECTION TYPE

B = Spline Curve

S = Straight Lines

• VERTICAL AXIS

X-Coordinate of the vertical reflection axis.

• HORIZONTAL AXIS

Y-Coordinate of the horizontal reflection axis.

• PERTURBATION FACTOR (optional)

Maximum distance stroke or stroke points may be shifteduring a single mutation.

• MUTATION RATE (optional)

Probability that the stroke will be mutated.

Table 1: This table describes how to interpret the ASCIItext of a drawing genotype. The genotype is a “blue-print” that defines how to construct the drawing.

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input mode, the user specifies one or more input drawings to use for creating the initial population.

3.2.1 Starting With Randomly GeneratedDrawings

In random mode, the computer initially creates a“screenful” of random drawings (20 in the current version, since that is how many fit comfortably on a 17 incworkstation screen). The user can repeatedly requesnew set of drawings until interesting ones are founRandom drawings may also be requested at any timduring the evolution process, typically to be used fomating with an evolved drawing. Examples of four initial random drawings are shown in Figure 2. Figureshows some drawings that were evolved usingrandommode. The butterfly forms were created with no preconceived goal in mind and took only a few minutes to produce. The face drawings were created with the specgoal of producing something face-like. This task proveto be much more difficult, taking over an hour to accomplish. However, once a face did emerge, it was quieasy to produce interesting variations on it. This expeence motivated the addition ofuser-input modedescribed below.

3.2.2 Starting With a User-Input DrawingIn user-input mode, the user provides one or more

drawings as a starting point. In this way, the systebegins with a coarse solution (or a set of them), and tsearch is immediately focussed on a potentially ric

Figure 2: Randomly generated drawings. These fourdrawings were produced automatically, with randomchoices made for the number of strokes, the strokepoints, and stroke parameters. A set of drawings likethese may be used as an initial population.

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area. If only one input drawing is given, it and a screeful of its mutated children make up an initial populationIf none of the displayed children are sufficiently interesing, the user may reselect the initial drawing for mutation repeatedly to view new sets of mutated childreSince one can produce new sets of children from tsame parent(s) repeatedly, the population size at ageneration may be as large as the user desires.

The computed “average face” (from [Bre86]) showat the top of Figure 4 was used in experiments as an itial input drawing and is thesole original ancestorof alldrawings presented in the remainder of this paper.1 Anaverage face is a useful initial input drawing becauseprovides a central point for evolving a variety of different faces. Faces in general are an ideal subject matterinteractive evolution because of the specialized (bpoorly understood) human ability to recognize and prcess them. The decision to limit examples to faceevolved from a single ancestor drawing was madeclarify the point that the variation achieved is a produof the interactive evolution process and not the resultstarting from varied drawings. Figure 4 shows an assoment of face drawings evolved from the average face aillustrates the wide variation that can be achieved withe system. These drawings were produced from taverage face by a simple sequence of mutations and mings. They were typically evolved in fifteen to one hun

1. Brennan calculated average features from a large set of face draings of real people, and constructed this “average face” for use in woon automating the creation of facial caricatures [Bre85].

Figure 3: Drawings evolved from random drawings. Theface drawings are related to each other (i.e., they wereevolved during the same session), as are the butterflyforms.

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Figure 4: The average face (a) and eleven of its descendants (b-l) . These drawings illustrate thewide variation possible in drawings evolved from a single ancestor. Drawings (b-f) were evolvedby 5 different users and include generation counts (available because these drawings were createdafter the automatic generation counter was installed). These users were able to mate their evolveddrawings with some previously evolved library images, which accounts for the high generationcounts of (b, c, and d). (The library images were also all descendants of the average face, andincluded drawing f .) The evolved drawings range in complexity from very simple (i) to boldcharcoal-like sketches (h). Eyes can be lit with shiny highlights (l), some faces appear distinctlymale (g) and others female (d, h). Some faces appear angry (g, i), pleasant (c, l), or horrified (j).

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dred generations, usually taking five to thirty minutes tcreate. Once a library of faces had been evolved, mainteresting new faces were produced quickly by combiing them. The number of generations required is vaable and depends heavily on the user’s goals aintentions. Some example generation counts are givenFigure 4. These counts were computed by initializinthe average face to a count of zero, increasing the pent’s count by one for a mutated child, and increasinthe count of the higher valued parent by one for a chiproduced by mating. Generation counts for drawingmated with library images thus include the number ogenerations required to create the library images, evthough they were already evolved when the user beg

3.3 Mutation OperatorA mutated “child” drawing is produced from the

genotype of its “parent” by randomly translating, adding, or deleting stroke points or entire strokes, and brandomly modifying stroke parameters (see Table 1Random translations are effectively a means of jigglinthe strokes, much as an artist might do when trying ovarious small adjustments to the lines in a sketch. Modfying stroke parameters, on the other hand, generacauses more substantial structural changes to the dring (for example, imagine changing the symmetry property of a stroke from vertical to horizontal). Theprobability of each type of mutation is individually con-trolled with adjustable system parameters. By settinthe probability of a given mutation type to zero, it is possible to turn off that type of mutation altogether. Wheevolving drawings inrandom mode, all mutation typeswere used; inuser-input mode, stroke parameter muta-tions were turned off. With the average face as an inpdrawing, it was not useful to change the basic propertiof the original face (for example, since one normallalways wants two eyes, two ears, etc., it didn’t maksense to allow changes to the symmetry properties offace). By limiting mutations to those that jiggled arounexisting lines without changing basic properties, it wapossible to retain the ‘face-ness’ of the original drawinwhile still producing a great deal of interesting variation

The genotype specifies aperturbation factorfor eachstroke that restricts the distance (in pixels) that thstroke or stroke point may be moved during a singmutation. The genotype also specifies a mutation raindicating the probability that a given stroke will bemutated during reproduction. Inuser-input modetheperturbation factors and mutation rates for the inpdrawing may be tailored to the specific subject matteFor example, the hair outline of the average face wgiven a larger perturbation factor than the eyes and othsmall features. If the small features are given too large

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perturbation factor, they can jump off the face in onmutation. If the hair outline is given too small a perturbation factor, it’s difficult to get any significant variationfrom the bathing-cap look of the original drawing. Themutation rate for the hair was also set higher than fother parts of the drawing. The perturbation factors anmutation rates can also be mutated, but we have notexperimented with this capability2.

Figure 5 shows an example set of single step mutions starting with the average face. This is how the intial screen might look when the average face is usedthe input drawing. The only active mutation types in thiexample are stroke deletions and translations, and poadditions, deletions, and translations, with translatiomore likely than other mutation types. Figure 6 illustrates the subtle mood and expression changes thatmutation operator can produce in a more compleevolved face.

3.4 Mating OperatorsA mating operator takes two parent drawings as inp

and uses them to produce a child drawing. The basapproach is to choose a subset of the strokes from eparent and combine them to form the child. We expermented with a number of different mating operatorthree of which are used in the current system. There atwo primary mating schemes (referred to asuniformmatingandID-based mating) and a third hybrid schemewhich combines the other two.

3.4.1 Uniform MatingIn uniform mating, each stroke of each parent is inde

pendently considered for inclusion in the child. If a faicoin is used to make the inclusion decision, this procewill typically produce a child with approximately half ofthe strokes from each parent. Since this is not necessathe desired outcome, we randomly choose a weight (bias) for the coin, with a different weight chosen foeach parent. If the weight is chosen to be any valubetween 0 and 1, the number of strokes in the chimight be as many as the sum of the number of strokesthe parents, or as few as one. Hence, a child may lomuch more or much less complicated than its parents.practice, we chose to limit the weights to values betwe.3 and .7, so that the child may differ in complexity fromits parents, but not too drastically. Depending on the coweights chosen, the child may have more in commowith one parent than the other. Figure 7 illustrates unform mating.

2. This might be useful for automatic tailoring of perturbation factorand mutation rates.

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Figure 5: Example of single step mutations starting with the average face. The original average face is shownThis figure is a segment of the screen from an actual run of the system in which the average face was given as adrawing. Note that small changes in just a few strokes of a drawing can subtly change the appearance of the faexample, the eyelid mutations in (m) give it a sleepy expression, the point translations in the jaw line of (b) makjaw more angular and masculine, and the eyebrow mutations in (g) create bushy eyebrows.

Figure 6: Mutations from an evolved face (parent left, children a b c). These particular mutations cause subtleences in facial expression, especially around the eyes. The mutations in (b) create an angrier look than the pwhereas (a) has a softer friendlier appearance. The eyebrow mutations in (c) create a slightly perplexed look

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Figure 7: Uniform mating (parents above, children below). Note that the children can inherit components of indual features or expression from both parents. In this case, parent (a) has an angry expression and looks mothan female, while parent (b) looks female. Child (c) inherited some of the angry expression from (a), but the doutlined eyes and curly lower hairline from (b), making it look more feminine. Assessments of this kind are csubjective.

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Figure 8: ID-based mating (parents above, children below). Children receive (for example) the entire set of eystrokes from parent (a) or from parent (b). This is in contrast to uniform mating, where the eyes are more likelya mix of the strokes from both parents. Child (d) has inherited the hair, eyebrows, and mouth from parent (a), beyes and lack of ears from parent (b), creating an entirely new look.

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3.4.2 ID-Based MatingUniform mating tends to produce interesting high

lighting and shading effects because drawings are cobined at a fine level of granularity (e.g., a subset ostrokes from one parent’s nose are combined with a suset of the strokes from the other parent’s nose) abecause similar strokes can be overlapped (e.g., the chmay receive eyelid strokes from both parents). Howevthere is no knowledge of individual drawing feature(such as the nose or eyes). In contrast, ID-based matmakes it possible to combine the parents at a coarlevel, allowing, for example, the child to inherit themother’s nose in its entirety and the father’s eyes in theentirety. To accomplish this, an ID field was added to thstroke representation. With the introduction of an IDfield, a user can indicate that certain strokes in an inpdrawing are related (such as those for the eyes) by ging them the same ID. If no IDs are provided, it isassumed that each stroke has a unique ID, which is thassigned automatically. IDs were added to the represtation for the average face such that the set of strokcomposing each facial feature shared the same ID.mate two drawings, corresponding sets of strokes (i.those with the same ID) from the parents are considerin turn, and one parent is chosen at random to contribuits entire set to the child. If only one parent contains a sof strokes with a particular ID (for example, if the fathehas ears, but the mother doesn’t), a random decisionmade as to whether to include that set in the child. Fiure 8 illustrates ID-based mating.

3.4.3 Hybrid MatingHybrid matingwas developed in an effort to com-

bine the best features of uniform and ID-based matinFor each set of corresponding strokes in the parents, idecided randomly whether to apply uniform mating oID-based mating within that set. This mating operatocould easily produce a child with the entire set of eystrokes from the mother, the entire set of nose strokfrom the father, and some combination of mouth strokfrom each.

4 DiscussionInteractive evolution relies heavily on the user, s

each person’s experience is different. This section prsents some observations on what it feels like to use tDrawing Evolver and an assessment of some of the fators that contribute to its successes and failures.

Since most of the user’s time is spent evaluatindrawings, it is important to provide a high quality set ocandidate solutions at each generation. The set of draings presented to the user should be sufficiently variebut must also stay within the user’s space-of-intere

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These two goals can be in opposition, because allowimore variation among a population of drawingincreases the probability that the successful parts of tdrawings will be lost as well. Primary factors influencing the quality of candidate solutions are the mutatiorates, characteristics of the mating operators, andgenetic representation.

In the Drawing Evolver, a lack of sufficient variationmeans that the user is sometimes faced with a screenof drawings whose differences are very subtle, makingdifficult to distinguish between them and to makchoices. The problem is most evident when using thmutation operator or when mating two very similar paent drawings. To combat this problem with respectmutation, we use outrageously high mutation rates (e.25%) as compared to the tiny fractional rates used in trditional GAs (or that occur in biological evolution).With respect to mating, having a library of evolveddrawings that can be mated with newly evolved drawings was found to be one helpful way to provide the uswith a more varied meta-population. Nonetheless, tsmall viewing sample size and the fact that the viewinsample is composed of children from just one or twparents can still conspire to decrease variation.

The mating operators themselves were also foundhave an impact on the variation and quality of drawingWhile experimenting with different mating operators, iwas somewhat surprising to discover that uniform maing worked well when applied to mutations of the aveage face. Interesting effects, such as shading ahighlighting, began to emerge in the evolved drawingand the level of intricacy of a drawing increased. Ifface acquired two corresponding strokes (e.g., two nostrokes), this often had the effect of producing a sketcversion of the face, with a more three-dimensional antextured look. These are effects that an artist can wovery hard to achieve in a drawing, and seeing theemerge in drawings produced by a computer is veexciting. However, because uniform mating embodiesknowledge of the high-level drawing components, it totends to produce sets of children whose differences asubtle and less dramatic. ID-based mating offers mosubstantial and dramatic perceptual differences, butless likely to produce the shading and highlightineffects. Combining both mating types makes it possibto achieve better variation without also losing the inteesting effects made possible with uniform mating.

The appropriateness of the representation for the pticular search space is yet another factor influencing tquality of the candidate solutions. The representatioused in the Drawing Evolver is very general (i.e., it carepresent any drawing at all) compared to Caldwell anJohnston’s (whose genotype represents faces on

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[CJ91]. But to limit our search to faces, the user must fiter out the drawings that don’t look face-like, whileCaldwell and Johnston’s users always see only vafaces. Of course, having a larger search space is alsoadvantage, providing the potential to evolve faciaexpressions and cartoon or animal faces, as well as mother things. Thus, the choice of representation is liketo involve a trade-off between focussing the searcspace or narrowing the solution space.

The system is easier and more pleasant to usebrowsing than it is for evolving a specified goal imageThis is no surprise, since the space contains a myriadinteresting drawings, but a comparatively small numbthat meet some narrowly specified set of requiremenThe narrower the goals, the tougher the search probleCaldwell and Johnston report success at using their stem to evolve a likeness to a criminal suspect, but thuse a very tightly constrained search space, and a getype designed with this specific purpose in mind. ThDrawing Evolver, with its larger and less constrainesearch space, is not particularly good at this tasAttempts to start with the average face and evolve a resonable likeness to a particular person were not vesuccessful. On the other hand, more loosely specifigoals, such as a plan for gender and facial expresswere fairly easy to carry out.

5 ConclusionsThe Drawing Evolver allows a user to produce

wide variety of complex sketches with highlighting anshading from a single very simple ancestor drawinThese drawings would be quite difficult and tedious (not impossible) to produce with most conventionaobject-oriented computer-aided drawing tools. The usin a reactive rather than proactive role, is responsibonly for selecting among sets of drawings produced bthe computer. The user need not have any drawing skor eye-hand coordination (at least no more than is necsary for selecting drawings with the mouse), but gooobservational and visual skills are useful to be abledistinguish between sometimes subtly different drawings. The user forfeits absolute control over the oucome, but gains an extended repertoire of possibresults.

Interactive evolution is an important new tool fousing the computer as a creative collaborator in thexploration of large search spaces. While empoweredhuman-evaluated fitness testing, it is also limited by thslow pace of interactive use. Despite its limitationsinteractive evolution has again proved a powerful toola new setting, adding more evidence to suggest thatfull potential is yet to be explored.

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6 AcknowledgementsThanks to Jim Clark, Ted Nesson, Joe Marks, Ka

Sims, Steve Smith, and Peter Todd for helpful discusions and encouragement. And special thanks to KSims, whose work inspired this research.

7 References

[Ba93] E. Baker. Summary: Evolving Line Drawings,Proceedings of the Fifth International Conference onGenetic Algorithms, 1993. Morgan Kaufmann Publish-ers.

[BS93] E. Baker and M. Seltzer. Evolving Line Draw-ings.Harvard University Center for Research in Com-puting Technology, Technical Report-21-93,1993.

[Bre85] S. Brennan. Caricature Generator: The DynamExaggeration of Faces by Computer.Leonardo, Vol. 18,No.3, pp. 170-178, 1985.

[Bre86] S. Brennan. Cited in Computer Recreations byA. K. Dewdney.Scientific American, Vol. 225, October1986.

[CJ91] C. Caldwell and V. S. Johnston. Tracking a Criminal Suspect Through Face Space With a Genetic Algorithm. Proceedings of the Fourth InternationalConference on Genetic Algorithms, pages 416-421,1991. Morgan Kaufmann Publishers.

[Daw87] R. Dawkins.The Blind Watchmaker. W.W.Norton and Company, New York, London, 1987.

[Gol89] D. E. Goldberg.Genetic Algorithms in SearchOptimization and Machine Learning. Addison-Wesley,1989.

[LP89] A. Lindenmeyer and P. Prusinkiewicz. Developmental Models of Multicellular Organisms: A ComputerGraphics Perspective. InArtificial Life, edited by C. G.Langton, Proc. Vol. VI, pages 221-250. Addison-Wesley1989.

[Opp89] P. Oppenheimer, The Artificial Menagerie. InArtificial Life, edited by C. G. Langton, Proc. Vol. VI,pages 221-250. Addison-Wesley, 1989.

[Sim91] K. Sims. Artificial Evolution for ComputerGraphics.Computer Graphics, 25(4):319-328, July1991.

[Sth91] J. R. Smith. Designing Biomorphs with an Interactive Genetic Algorithm,Proceedings of the FourthInternational Conference on Genetic Algorithms, pages535-538, 1991. Morgan Kaufmann Publishers.

[[TL92] S. Todd and W. Latham.Evolutionary Art andComputers. Academic Press: Harcourt, Brace, Jovanovich, 1992.

Evolving Line Drawings

Ellie BakerMargo Seltzer

Aiken Computation Laboratory

Harvard University

33 Oxford Street

Cambridge, MA 02139

e-mail: ellie@das.harvard.edu

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AbstractThis paper explores the application of interactiv

genetic algorithms to the creation of line drawings. Whave built a system that mates or mutates drawinselected by the user to create a new generation of draings. The initial population from which the user makeselections may be generated randomly or input manally. The process of selection and procreation is repeamany times to evolve a drawing. A wide variety of complex sketches with highlighting and shading can bevolved from very simple drawings. This technique hapotential for augmenting and enhancing the powertraditional computer-aided drawing tools, and foexpanding the repertoire of the computer-assisted arti

Keywords: Genetic Algorithm, Interactive Evolu-tion, Interactive Graphics.

1 IntroductionInteractive fitness evaluation for genetic algorithm

was introduced by zoologist Richard Dawkins in hibook, “The Blind Watchmaker.” The book describescomputer program for evolving images of creatureDawkins calls “biomorphs” [Daw87]. Each biomorph isproduced from a compact genetic code that specifiescreature’s particular characteristics. Contained in thcode is the essential information necessary to creatbit-mapped image of the creature on the computscreen. Using a technique calledinteractive evolution,the computer and user collaborate to produce compand varied insect-like creatures. With interactive evolution, the user selects the most aesthetically pleasing bmorph from a set of creatures displayed on the screeBy randomly mutating the genetic code of the selectebiomorph, the computer creates a new generation of bmorphs to display. The new generation is again sujected to the aesthetic selection criteria of the user

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produce the following generation. This cycle continueuntil the user “evolves” a pleasing creature.

Dawkins’ idea has spawned several successful appcations of interactive evolution to other image desigand construction problems ([Sim91], [CJ91], [TL92][Sth91], [BS93], [Ba93]). This paper presents an interative evolution system for creating line drawings. Thgenetic representation and definition of the genetic opators are substantially different from previous interactivevolution systems, and it produces correspondingunique results. The user interface is similar to other sytems (especially Sims’ [Sim91] and Dawkins’ [Daw87])An initial population of drawings, either generated randomly by the computer or input by the user, is displayeon the screen. From the displayed set the user selectsdrawing for mutation or two drawings for mating. Themating and/or mutation operations are applied to thselected drawings to produce a new set of progeny draings that supply the input for the next round of useselection. This process is repeated multiple times“evolve” a drawing of interest to the user. Evolved drawings may be saved and later recalled for mating wiother evolved drawings.

2 Interactive EvolutionInteractive evolution provides a powerful new tech

nique for enabling human-computer collaboration. Itpotentially applicable to a wide variety of search problems, provided the candidate solutions can be producquickly by a computer and evaluated quickly and easiby a human. Since humans are often very good at pcessing and assessing pictures quickly, interactive evotion is particularly well suited to search problems whoscandidate solutions can be represented visually.

Traditionally a genetic algorithm (GA) requires thespecification of survival fitness criteria to be evaluateby the computer [Gol89]. This is typically one of themost difficult tasks in designing a GA. With interactive

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evolution the user performs this step, applying whatevcomplicated measure of fitness is desired. Unfortunateincluding a human evaluator also severely weakens tGA because the human’s speed and patience beconew limiting factors, restricting the population size anpossible number of generations. Despite this drawbainteractive evolution has been used to produce somastounding results that could not have been achieveasily by any other known method [Sim91], [TL92].

The beauty of interactive evolution is that the useneed onlyapply personal fitness criteria, not state oeven understand them. This feature of interactive evotion is used very effectively in a system by Caldwell anJohnston for allowing a crime victim to produce a faciacomposite of a criminal suspect [CJ91]. This systetakes advantage of the remarkable human ability to reognize faces. A database of face parts (e.g., eyes, nomouths) is used to construct candidate faces, which athen rated by a human operator for their degree of likness to the suspect. Based on these ratings, the facesrecombined and mutated to produce a new generationfaces. This rating and procreation process is repeauntil a likeness to the suspect is achieved. Caldwell aJohnston state that, while “humans have excellent facrecognition ability,” they “have great difficulty in recall-ing facial characteristics with sufficient detail to providan accurate composite” [CJ91]. This is a perfect example of the ability to apply a complex fitness test withouconsciously understanding it. Since computers have htorically performed poorly at face recognition (compared to humans), the system employs a particulasuitable division of labor between human and comput

Sims’ system uses interactive evolution to creabeautiful, abstract color images [Sim91]. The genetcode for an image is a Lisp expression representingsequence of image-processing functions (i.e., functiothat take as input a set of pixel values and associacoordinates, and produce new pixel values as outpuSims uses a fixed set of image-processing primitiveand uses interactive evolution to evolve increasingcomplex functions. The search space consists of all Liexpressions that can be constructed from the primitiveThe mutation and mating operators restructure or moify the Lisp expressions and make random paramechanges. Sims also evolves plant forms by applyininteractive evolution to L-Systems, grammars thadescribe biological models of plant growth [Sim91[LP89].

Other interactive evolution systems of note includeDawkins’, which uses a recursive genetic structure thproduces varied, but highly characteristic insect-likforms [Daw87]; Todd and Latham’s, which uses constructive solid geometry techniques to evolve “virtua

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sculptures” [TL92]; Smith’s, which uses a Fourierseries-based representation to produce bug-like curvline forms [Sth91]; and Oppenheimer’s, which producelife-like 3D tree forms using a recursive fractal represetation similar to Dawkins’ [Opp89].

These systems use a variety of genetic representions to explore both infinite and large finite spaceEach system relies on the ability to represent candidasolutions visually and on the human ability to evaluathese solutions quickly and in parallel. The evaluatiocriteria used are difficult-to-articulate personalizeassessments of such poorly defined characteristics“interesting,” “aesthetically beautiful,” “good likeness,”or “life-like.” These are terms whose definitions mavary drastically from person to person or even chanfrom moment to moment in the same person. It is thtype of search problem for which interactive evolutioprovides an exciting new tool.

To date only a handful of interactive evolution applications have been built. These few applications hashown interactive evolution to be an interesting and usful tool, but there is still untapped potential in manyother areas as well. One goal of this research is to addcurrent understanding of interactive evolution by applying it in a new domain. In doing so we hope to gain fresinsight into both its power and its limitations.

At the application level, the goal is to build a newkind of computer-aided drawing tool. Traditional toolsuse a compact, object-oriented drawing representatthat makes it easy for a user to apply many operationsthe drawing. Unlike a bit-mapped image, this high-leverepresentation allows the user easy manipulation of invidual drawing features, such as the ability to deletemodify individual lines, points, or other objects. However, creation of drawings in this format requires vergood eye-hand coordination, and, for anything eveslightly complex, a great deal of effort and tediumUsing these tools to create drawings beyond a certalevel of complexity can be all but impossible, especiallfor a non-artist. The work presented here is intendeddemonstrate the potential for using interactive evolutioto augment and enhance the power of traditional computer-aided drawing tools and to expand the repertoirethe computer-assisted artist.

Section 3 describes the Drawing Evolver and showhow interactive evolution can be used to create compldrawings in a high-level format. Section 4 discusses oexperience using the Drawing Evolver.

3 Drawing EvolverThe Drawing Evolver is an interactive evolution sys

tem written in the C programming language that rununder X Windows on a Unix workstation. Its basic com

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ponents are: a structured representation for drawingsmeans of producing an initial population of drawingsmating and mutation operators; and an operator that pduces a drawing from its corresponding genetic code.

3.1 Drawing RepresentationIn the language of biologists, the genetic constitutio

of an organism is referred to as itsgenotype, and thephysical manifestation of the organism as itsphenotype.In the Drawing Evolver, the organisms are drawingwhose appearance (or phenotype) is determined by thgenetic code (or genotype). The core of the system is tstructure of the genotypes used to represent drawingsgenotype consists of an ordered set of “strokes,” wheeach stroke is represented by an ordered set of poiand a method for connecting them (e.g., a spline curor straight lines). A stroke may be loosely thought of aa mark made by a pencil without lifting it off the pageThe stroke specification also includes other per-stroparameters. For example, a symmetry type (horizontvertical, both, or neither) is given for each stroke. A seof symmetric marks are encoded as a single mark withstated symmetry type. In this case several disconnecmarks are still referred to as one stroke. The numberstrokes in a drawing, as well as the number of points instroke, can vary, resulting in genotypes of varyinlength, and drawings of varying complexity.

In theory, this representation provides for the possbility of any line drawing contained within the drawingframe. The number of possible phenotypes is very largbut finite. Since there are only two possible values (onoroff) for every pixel in the drawing frame, and we use250 by 250 pixel square frame, there are a total

distinct phenotypes (over 18000 orders of magnitude larger than Caldwell and Johnston’s search spaof 34 billion possible composites [CJ91]). Figure 1 illus

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Figure 1: A simple example drawing and its corre-sponding genotype (below it). Although the drawinghas three separate marks, it consists of only twostrokes. The jagged stroke has the property of being“horizontally symmetric,” so it consists of two separatemarks mirroring each other across a vertical axis.

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trates a simple drawing and its corresponding genotypTable 1 specifies how drawing genotypes are interprete

To mutate a drawing, randomly chosen strokesstroke points are moved, deleted, or added, and stroparameters are randomly modified. When two drawinare mated, a randomly chosen subset of strokes froeach of the parent drawings are combined to form a nedrawing. These operations are described in more dein Section 3.3 and Section 3.4.

3.2 Operation ModesThe system has two modes of operation that diff

with respect to how an initial population of drawings icreated. Inrandom mode, the computer creates an initiapopulation of randomly generated drawings. Inuser-

INTERPRETING A DRA WING GENOTYPE

Each stroke is represented by two consecutive lines of teone for the stroke parameters, and one giving an ordered sethe x,y coordinates of the stroke points (i.e., x1 y1 x2 y2 xy3). The stroke parameters are as follows:

• ID (optional)

An identifier for the stroke.

• STROKE TYPE

O = Open (first and last points are not connected)

S = Space Enclosing (first and last points are connected

G = Glued to Next Stroke (last point is connected to firstpoint of next stroke)

• SYMMETRY TYPE

N = No Symmetry

V = Vertical Symmetry

H = Horizontal Symmetry

A = Vertical and Horizontal Symmetry

• POINT CONNECTION TYPE

B = Spline Curve

S = Straight Lines

• VERTICAL AXIS

X-Coordinate of the vertical reflection axis.

• HORIZONTAL AXIS

Y-Coordinate of the horizontal reflection axis.

• PERTURBATION FACTOR (optional)

Maximum distance stroke or stroke points may be shifteduring a single mutation.

• MUTATION RATE (optional)

Probability that the stroke will be mutated.

Table 1: This table describes how to interpret the ASCIItext of a drawing genotype. The genotype is a “blue-print” that defines how to construct the drawing.

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input mode, the user specifies one or more input drawings to use for creating the initial population.

3.2.1 Starting With Randomly GeneratedDrawings

In random mode, the computer initially creates a“screenful” of random drawings (20 in the current version, since that is how many fit comfortably on a 17 incworkstation screen). The user can repeatedly requesnew set of drawings until interesting ones are founRandom drawings may also be requested at any timduring the evolution process, typically to be used fomating with an evolved drawing. Examples of four initial random drawings are shown in Figure 2. Figureshows some drawings that were evolved usingrandommode. The butterfly forms were created with no preconceived goal in mind and took only a few minutes to produce. The face drawings were created with the specgoal of producing something face-like. This task proveto be much more difficult, taking over an hour to accomplish. However, once a face did emerge, it was quieasy to produce interesting variations on it. This expeence motivated the addition ofuser-input modedescribed below.

3.2.2 Starting With a User-Input DrawingIn user-input mode, the user provides one or more

drawings as a starting point. In this way, the systebegins with a coarse solution (or a set of them), and tsearch is immediately focussed on a potentially ric

Figure 2: Randomly generated drawings. These fourdrawings were produced automatically, with randomchoices made for the number of strokes, the strokepoints, and stroke parameters. A set of drawings likethese may be used as an initial population.

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area. If only one input drawing is given, it and a screeful of its mutated children make up an initial populationIf none of the displayed children are sufficiently interesing, the user may reselect the initial drawing for mutation repeatedly to view new sets of mutated childreSince one can produce new sets of children from tsame parent(s) repeatedly, the population size at ageneration may be as large as the user desires.

The computed “average face” (from [Bre86]) showat the top of Figure 4 was used in experiments as an itial input drawing and is thesole original ancestorof alldrawings presented in the remainder of this paper.1 Anaverage face is a useful initial input drawing becauseprovides a central point for evolving a variety of different faces. Faces in general are an ideal subject matterinteractive evolution because of the specialized (bpoorly understood) human ability to recognize and prcess them. The decision to limit examples to faceevolved from a single ancestor drawing was madeclarify the point that the variation achieved is a produof the interactive evolution process and not the resultstarting from varied drawings. Figure 4 shows an assoment of face drawings evolved from the average face aillustrates the wide variation that can be achieved withe system. These drawings were produced from taverage face by a simple sequence of mutations and mings. They were typically evolved in fifteen to one hun

1. Brennan calculated average features from a large set of face draings of real people, and constructed this “average face” for use in woon automating the creation of facial caricatures [Bre85].

Figure 3: Drawings evolved from random drawings. Theface drawings are related to each other (i.e., they wereevolved during the same session), as are the butterflyforms.

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Figure 4: The average face (a) and eleven of its descendants (b-l) . These drawings illustrate thewide variation possible in drawings evolved from a single ancestor. Drawings (b-f) were evolvedby 5 different users and include generation counts (available because these drawings were createdafter the automatic generation counter was installed). These users were able to mate their evolveddrawings with some previously evolved library images, which accounts for the high generationcounts of (b, c, and d). (The library images were also all descendants of the average face, andincluded drawing f .) The evolved drawings range in complexity from very simple (i) to boldcharcoal-like sketches (h). Eyes can be lit with shiny highlights (l), some faces appear distinctlymale (g) and others female (d, h). Some faces appear angry (g, i), pleasant (c, l), or horrified (j).

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dred generations, usually taking five to thirty minutes tcreate. Once a library of faces had been evolved, mainteresting new faces were produced quickly by combiing them. The number of generations required is vaable and depends heavily on the user’s goals aintentions. Some example generation counts are givenFigure 4. These counts were computed by initializinthe average face to a count of zero, increasing the pent’s count by one for a mutated child, and increasinthe count of the higher valued parent by one for a chiproduced by mating. Generation counts for drawingmated with library images thus include the number ogenerations required to create the library images, evthough they were already evolved when the user beg

3.3 Mutation OperatorA mutated “child” drawing is produced from the

genotype of its “parent” by randomly translating, adding, or deleting stroke points or entire strokes, and brandomly modifying stroke parameters (see Table 1Random translations are effectively a means of jigglinthe strokes, much as an artist might do when trying ovarious small adjustments to the lines in a sketch. Modfying stroke parameters, on the other hand, generacauses more substantial structural changes to the dring (for example, imagine changing the symmetry property of a stroke from vertical to horizontal). Theprobability of each type of mutation is individually con-trolled with adjustable system parameters. By settinthe probability of a given mutation type to zero, it is possible to turn off that type of mutation altogether. Wheevolving drawings inrandom mode, all mutation typeswere used; inuser-input mode, stroke parameter muta-tions were turned off. With the average face as an inpdrawing, it was not useful to change the basic propertiof the original face (for example, since one normallalways wants two eyes, two ears, etc., it didn’t maksense to allow changes to the symmetry properties offace). By limiting mutations to those that jiggled arounexisting lines without changing basic properties, it wapossible to retain the ‘face-ness’ of the original drawinwhile still producing a great deal of interesting variation

The genotype specifies aperturbation factorfor eachstroke that restricts the distance (in pixels) that thstroke or stroke point may be moved during a singmutation. The genotype also specifies a mutation raindicating the probability that a given stroke will bemutated during reproduction. Inuser-input modetheperturbation factors and mutation rates for the inpdrawing may be tailored to the specific subject matteFor example, the hair outline of the average face wgiven a larger perturbation factor than the eyes and othsmall features. If the small features are given too large

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perturbation factor, they can jump off the face in onmutation. If the hair outline is given too small a perturbation factor, it’s difficult to get any significant variationfrom the bathing-cap look of the original drawing. Themutation rate for the hair was also set higher than fother parts of the drawing. The perturbation factors anmutation rates can also be mutated, but we have notexperimented with this capability2.

Figure 5 shows an example set of single step mutions starting with the average face. This is how the intial screen might look when the average face is usedthe input drawing. The only active mutation types in thiexample are stroke deletions and translations, and poadditions, deletions, and translations, with translatiomore likely than other mutation types. Figure 6 illustrates the subtle mood and expression changes thatmutation operator can produce in a more compleevolved face.

3.4 Mating OperatorsA mating operator takes two parent drawings as inp

and uses them to produce a child drawing. The basapproach is to choose a subset of the strokes from eparent and combine them to form the child. We expermented with a number of different mating operatorthree of which are used in the current system. There atwo primary mating schemes (referred to asuniformmatingandID-based mating) and a third hybrid schemewhich combines the other two.

3.4.1 Uniform MatingIn uniform mating, each stroke of each parent is inde

pendently considered for inclusion in the child. If a faicoin is used to make the inclusion decision, this procewill typically produce a child with approximately half ofthe strokes from each parent. Since this is not necessathe desired outcome, we randomly choose a weight (bias) for the coin, with a different weight chosen foeach parent. If the weight is chosen to be any valubetween 0 and 1, the number of strokes in the chimight be as many as the sum of the number of strokesthe parents, or as few as one. Hence, a child may lomuch more or much less complicated than its parents.practice, we chose to limit the weights to values betwe.3 and .7, so that the child may differ in complexity fromits parents, but not too drastically. Depending on the coweights chosen, the child may have more in commowith one parent than the other. Figure 7 illustrates unform mating.

2. This might be useful for automatic tailoring of perturbation factorand mutation rates.

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Figure 6: Mutations from an evolved face (parent left, children a b c). These particular mutations cause subtleences in facial expression, especially around the eyes. The mutations in (b) create an angrier look than the pwhereas (a) has a softer friendlier appearance. The eyebrow mutations in (c) create a slightly perplexed look

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Figure 7: Uniform mating (parents above, children below). Note that the children can inherit components of indual features or expression from both parents. In this case, parent (a) has an angry expression and looks mothan female, while parent (b) looks female. Child (c) inherited some of the angry expression from (a), but the doutlined eyes and curly lower hairline from (b), making it look more feminine. Assessments of this kind are csubjective.

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Figure 8: ID-based mating (parents above, children below). Children receive (for example) the entire set of eystrokes from parent (a) or from parent (b). This is in contrast to uniform mating, where the eyes are more likelya mix of the strokes from both parents. Child (d) has inherited the hair, eyebrows, and mouth from parent (a), beyes and lack of ears from parent (b), creating an entirely new look.

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3.4.2 ID-Based MatingUniform mating tends to produce interesting high

lighting and shading effects because drawings are cobined at a fine level of granularity (e.g., a subset ostrokes from one parent’s nose are combined with a suset of the strokes from the other parent’s nose) abecause similar strokes can be overlapped (e.g., the chmay receive eyelid strokes from both parents). Howevthere is no knowledge of individual drawing feature(such as the nose or eyes). In contrast, ID-based matmakes it possible to combine the parents at a coarlevel, allowing, for example, the child to inherit themother’s nose in its entirety and the father’s eyes in theentirety. To accomplish this, an ID field was added to thstroke representation. With the introduction of an IDfield, a user can indicate that certain strokes in an inpdrawing are related (such as those for the eyes) by ging them the same ID. If no IDs are provided, it isassumed that each stroke has a unique ID, which is thassigned automatically. IDs were added to the represtation for the average face such that the set of strokcomposing each facial feature shared the same ID.mate two drawings, corresponding sets of strokes (i.those with the same ID) from the parents are considerin turn, and one parent is chosen at random to contribuits entire set to the child. If only one parent contains a sof strokes with a particular ID (for example, if the fathehas ears, but the mother doesn’t), a random decisionmade as to whether to include that set in the child. Fiure 8 illustrates ID-based mating.

3.4.3 Hybrid MatingHybrid matingwas developed in an effort to com-

bine the best features of uniform and ID-based matinFor each set of corresponding strokes in the parents, idecided randomly whether to apply uniform mating oID-based mating within that set. This mating operatocould easily produce a child with the entire set of eystrokes from the mother, the entire set of nose strokfrom the father, and some combination of mouth strokfrom each.

4 DiscussionInteractive evolution relies heavily on the user, s

each person’s experience is different. This section prsents some observations on what it feels like to use tDrawing Evolver and an assessment of some of the fators that contribute to its successes and failures.

Since most of the user’s time is spent evaluatindrawings, it is important to provide a high quality set ocandidate solutions at each generation. The set of draings presented to the user should be sufficiently variebut must also stay within the user’s space-of-intere

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These two goals can be in opposition, because allowimore variation among a population of drawingincreases the probability that the successful parts of tdrawings will be lost as well. Primary factors influencing the quality of candidate solutions are the mutatiorates, characteristics of the mating operators, andgenetic representation.

In the Drawing Evolver, a lack of sufficient variationmeans that the user is sometimes faced with a screenof drawings whose differences are very subtle, makingdifficult to distinguish between them and to makchoices. The problem is most evident when using thmutation operator or when mating two very similar paent drawings. To combat this problem with respectmutation, we use outrageously high mutation rates (e.25%) as compared to the tiny fractional rates used in trditional GAs (or that occur in biological evolution).With respect to mating, having a library of evolveddrawings that can be mated with newly evolved drawings was found to be one helpful way to provide the uswith a more varied meta-population. Nonetheless, tsmall viewing sample size and the fact that the viewinsample is composed of children from just one or twparents can still conspire to decrease variation.

The mating operators themselves were also foundhave an impact on the variation and quality of drawingWhile experimenting with different mating operators, iwas somewhat surprising to discover that uniform maing worked well when applied to mutations of the aveage face. Interesting effects, such as shading ahighlighting, began to emerge in the evolved drawingand the level of intricacy of a drawing increased. Ifface acquired two corresponding strokes (e.g., two nostrokes), this often had the effect of producing a sketcversion of the face, with a more three-dimensional antextured look. These are effects that an artist can wovery hard to achieve in a drawing, and seeing theemerge in drawings produced by a computer is veexciting. However, because uniform mating embodiesknowledge of the high-level drawing components, it totends to produce sets of children whose differences asubtle and less dramatic. ID-based mating offers mosubstantial and dramatic perceptual differences, butless likely to produce the shading and highlightineffects. Combining both mating types makes it possibto achieve better variation without also losing the inteesting effects made possible with uniform mating.

The appropriateness of the representation for the pticular search space is yet another factor influencing tquality of the candidate solutions. The representatioused in the Drawing Evolver is very general (i.e., it carepresent any drawing at all) compared to Caldwell anJohnston’s (whose genotype represents faces on

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[CJ91]. But to limit our search to faces, the user must fiter out the drawings that don’t look face-like, whileCaldwell and Johnston’s users always see only vafaces. Of course, having a larger search space is alsoadvantage, providing the potential to evolve faciaexpressions and cartoon or animal faces, as well as mother things. Thus, the choice of representation is liketo involve a trade-off between focussing the searcspace or narrowing the solution space.

The system is easier and more pleasant to usebrowsing than it is for evolving a specified goal imageThis is no surprise, since the space contains a myriadinteresting drawings, but a comparatively small numbthat meet some narrowly specified set of requiremenThe narrower the goals, the tougher the search probleCaldwell and Johnston report success at using their stem to evolve a likeness to a criminal suspect, but thuse a very tightly constrained search space, and a getype designed with this specific purpose in mind. ThDrawing Evolver, with its larger and less constrainesearch space, is not particularly good at this tasAttempts to start with the average face and evolve a resonable likeness to a particular person were not vesuccessful. On the other hand, more loosely specifigoals, such as a plan for gender and facial expresswere fairly easy to carry out.

5 ConclusionsThe Drawing Evolver allows a user to produce

wide variety of complex sketches with highlighting anshading from a single very simple ancestor drawinThese drawings would be quite difficult and tedious (not impossible) to produce with most conventionaobject-oriented computer-aided drawing tools. The usin a reactive rather than proactive role, is responsibonly for selecting among sets of drawings produced bthe computer. The user need not have any drawing skor eye-hand coordination (at least no more than is necsary for selecting drawings with the mouse), but gooobservational and visual skills are useful to be abledistinguish between sometimes subtly different drawings. The user forfeits absolute control over the oucome, but gains an extended repertoire of possibresults.

Interactive evolution is an important new tool fousing the computer as a creative collaborator in thexploration of large search spaces. While empoweredhuman-evaluated fitness testing, it is also limited by thslow pace of interactive use. Despite its limitationsinteractive evolution has again proved a powerful toola new setting, adding more evidence to suggest thatfull potential is yet to be explored.

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6 AcknowledgementsThanks to Jim Clark, Ted Nesson, Joe Marks, Ka

Sims, Steve Smith, and Peter Todd for helpful discusions and encouragement. And special thanks to KSims, whose work inspired this research.

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