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Call for Participation: A programming competition involving Evolutionary Computation, Machine Learning, Math and Visual Arts

Exploring a Design Space for Patterns and Tilings
a periodical programming competition involving Evolutionary Computation, Machine Learning, Mathematics and Visual Arts (student contributions are welcome)
The goal of this periodical programming competition (with some price money) is the exploration of a special design space of patterning & tiling methods and to make selected points of this space (methods that generate interesting and aesthetic results) available for image editing so that contemporary and future artists, illustrators, comic artists, designers, ... can use them under an open source license (CC0 and the like).
The design space relevant for image editing is based on the idea that patterns and tiles can be generated by basic shapes (prototiles) together with a CRMT command list approach that repeatedly clone, rotate and mirror a prototile and translate it over a finite pattern plane. With this unified approach the centuries of mathematical and artistic knowledge about patters, tilings and ornaments will be usable in image editing for bitmap and vector images. By programming CRMT-image interpreter as standalone programs or Plug-ins in bitmap graphic programs (GIMP, Photoshop, ...) and vector graphic programs (Illustrator, Inkscape) very powerful artistic tools can be made that will be using a growing number of CRMT command lists to generate all sorts of patterns from input images.
The goal of the first competition is to reproduce known tilings from the Tiling Database ( This should be done by a learning process because such processes should be the basis for exploration the design space and its extensions in further iterations of the competition. Technically the learning of a CRMT command list for a tiling is viewed as a 2D packing problem with the 2-objective function "gap -> 0 & overlap -> 0".
The competition is originated in the field of Evolutionary Art therefore the learning process should be preferably done with Evolutionary Computation (EC) methods like Genetic Programming but it is not restricted to this if the learning environment with all aspects and parameters are published under an open source license so that others can use it for further exploration.
A detailed description of this programming competition with more technical details, winning conditions, price money, and future directions can be found under:  
Registration starts on 01.05.2015 and ends on 15.11.2016. Contributions can be made until 15.01.2016.
Comments and suggestions to all aspects of the competition project are appreciated and should be addressed to Dr. Günter Bachelier (guba at
Please redistribute this Call for Participation in appropriate mailing lists, blogs, tweets, ... to address and attract as much as possible developer, scientists and students in Evolutionary Computation, Machine Learning, Image Processing and Editing, Mathematics, Media Art, ...

please forward any questions to 
Entry Slideshow
Entry Slideshow

Hi, I have a question on classical variable-length recombination. I would like to combine heads and bodies, likewise illustrated in

Suppose a simple framework, which generates C programs. The statements look like

"STATEMENT -> IF  (BOOL) {BODY} | WHILE (BOOL) {BODY} | SetMem(Int, Int)".

Every BODY can have any number of such statements. I could take arbitrary parts of chromosome and crossover them. However, varying length hampers recombination of the substatements.

I mean that program is a tree, whose every non-terminal is an expression (aka function'). Everything is fine when expression length is fixed, like a + b + c + d. The replicator iterates the tree. It will flip a coin to decide whether to choose A from mother or father. It will then flip another coin for choosing B subexpression and so on. In case both parents bear the same gene, replicator will descend into the subtree to flip coins on mismatching subexpressions. Such kind of recombination maintains the common part of the parents (i.e. homology/alleles) and enables itself. Everything fine.

However, admitting variable length seem to break the homology. If parents are a + b + c and x + y + z + w then which of the loci are alleles to flip the coin? The transparency is lost.

Sorry for asking such basic stuff but I cannot find the answer anywhere and do not see a better group to ask. Somebody closes the ai@stacksxchange proposal all the time.
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