Description:
We present AutoMap, a pair of methods for automatic generation of evolvable genotype-phenotype mappings. Both use an artificial neural network autoencoder trained on phenotypes harvested from fitness peaks as the basis for a genotype-phenotype mapping. In the first, the decoder segment of a bottlenecked autoencoder serves as the genotype-phenotype mapping. In the second, a denoising autoencoder serves as the genotype-phenotype mapping. Automatic generation of evolvable genotype-phenotype mappings are demonstrated on the n-legged table problem, a toy problem that defines a simple rugged fitness landscape, and the Scrabble string problem, a more complicated problem that serves as a rough model for linear genetic programming. For both problems, the automatically generated genotype-phenotype mappings are found to enhance evolvability.

License: MIT License

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Find source code for this project on GitHub:
n-legged table problem domain: https://github.com/mmore500/cse-848-project
Scrabble problem domain: https://github.com/mmore500/scrabble_evo_autoencoder
Tutorials showing how to use software developed for this project are here:
n-legged table problem domain
scrabble problem domain
The PDF writeup for this project is available on GitHub:
* https://g...

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