Learning an Evolvable Genotype-Phenotype Mapping

  1. Wolfgang Banzhaf
  2. Charles Ofria

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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


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 writeup for this project is also available on GitHub: * https://...


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