Learning an Evolvable Genotype-Phenotype Mapping

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Date July 15th, 2018
DOI 10.1145/3205455.3205597
Venue The Genetic and Evolutionary Computation Conference

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.

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  author = {Moreno, Matthew Andres and Banzhaf, Wolfgang and Ofria, Charles},
  title = {Learning an Evolvable Genotype-Phenotype Mapping},
  year = {2018},
  isbn = {9781450356183},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3205455.3205597},
  doi = {10.1145/3205455.3205597},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
  pages = {983–990},
  numpages = {8},
  keywords = {deep learning, indirect encodings, evolvability, genetic algorithms, adaptive representations, genotype-phenotype map},
  location = {Kyoto, Japan},
  series = {GECCO '18}
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Matthew Andres Moreno, Wolfgang Banzhaf, and Charles Ofria. 2018. Learning an evolvable genotype-phenotype mapping. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘18). Association for Computing Machinery, New York, NY, USA, 983–990. https://doi.org/10.1145/3205455.3205597

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