CSE 848 Project

Experiments using autoencoders to learn evolvable encodings for the n-legged table problem.

Learning an Evolvable Genotype-Phenotype Map

Experiments reported in this paper employed v2.0.2 of this software.

data, tutorials, and writeup @ https://osf.io/n92c7/

Accepted to GECCO 2018.

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.

Software Authorship

Matthew Andres Moreno

mmore500@msu.edu

Credits

This implementation draws on several open-source packages, most notably Distributed Evolutionary Algorithms for Python (DEAP). Both the package and (adapted) example usage from the package's documentation were employed in this implementation.