To show how Genetic Programming of Pyevolve can be flexible, I’ve done a simple example using Adobe Flex and Pyevolve, the example is just to show how to evolve some kind of Flex layouts, I’ve not implemented the fitness function, this example will just create a random Flex layout using MXML. So, here is the Pyevolve code of the example:

import random from pyevolve import * def gp_hbox(x, y): return "%s %s" % (x,y) def gp_vbox(x, y): return "%s %s" % (x,y) def gp_panel(x, y): return "%s %s" % (x,y) def eval_func(chromosome): code_comp = chromosome.getCompiledCode() for a in xrange(0, 5): for b in xrange(0, 5): evaluated = eval(code_comp) return random.randint(1,100) def main_run(): genome = GTree.GTreeGP() genome.setParams(max_depth=5, method="ramped") genome.evaluator += eval_func ga = GSimpleGA.GSimpleGA(genome) button = repr("<mx:Button label='Button'/>") label = repr("<mx:Label text='Label'/>") text_input = repr("<mx:TextInput width='50'/>") ga.setParams(gp_terminals = [button, label, text_input], gp_function_prefix = "gp") ga.setMinimax(Consts.minimaxType["minimize"]) ga.evolve(freq_stats=5) print ga.bestIndividual() if __name__ == "__main__": main_run()

As you can see, I’ve created the layout tags like HBox, VBox and Panel as functions of GP and the Button, Labe, TextInput as terminals of the GP, the result is very funny, it’s just a random layout, but you can use your imagination to create some nice and interesting fitness functions.

Here is the SWF generated from a random individual of the population:

I hope you enjoyed =)