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Simulated annealing

annealing

Here’s an update on our earlier post about genetic programming. Altered Qualia has posted a new implementation of [Ron Alsing]‘s idea. It starts with 50 polygons and then randomly changes one parameter with each optimization step. If the the change results in fewer differences from the target image, it’s kept as the new best DNA. This search method is similar to simulated annealing. The image above is the result of 1500 good mutations out of 35900 possible. The implementation lets you choose any image, but smaller means the fitness calculation is faster. It’s written in JavaScript using the <canvas> environment. You’ll definitely get better performance using newer browser builds.

[Original image by R Stevens]

[via Waxy]

Genetic programming

monalisa

[Ron Alsing] wanted to try out some genetic programming, so he created a simple test problem: Could you render the Mona Lisa using just 50 semitransparent polygons? The program starts with a random DNA sequence. It then mutates and compares itself to the original image. If the mutation is closer, it becomes the new sequence. The final image he shows looks pretty good after 904,314 iterations.

[prunesquallor] pointed out a genetic algorithm project of his own. It’s a flash program to evolve a car. The car tries to get as far as possible on a set terrain without the passenger circles hitting the ground. The wheel size and positions can change along with the spring length, constant, and damping. A graph tracks the best performance along with the mean. He’s planning on building a version that lets you change the parameters.

[via Waxy]

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