Learning an evolvable genotype-phenotype mapping

Created by W.Langdon from gp-bibliography.bib Revision:1.9194

@InProceedings{Moreno:2018:GECCO,

• author = "Matthew Andres Moreno and Wolfgang Banzhaf and Charles Ofria",
• title = "Learning an evolvable genotype-phenotype mapping",
• booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2018",
• editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre",
• pages = "983--990",
• address = "Kyoto, Japan",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, linear genetic programming, adaptive representations, indirect encodings, genotype-phenotype map, evolvability, deep learning",
• isbn13 = "978-1-4503-5618-3",
• URL = "https://www.cmap.polytechnique.fr/~nikolaus.hansen/proceedings/2018/GECCO/proceedings/proceedings_files/pap523s3-file1.pdf",
• DOI = "doi:10.1145/3205455.3205597",
• URL = "https://mmore500.com/2018/05/23/automap.html",
• size = "8 pages",
• abstract = "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.",
• notes = "Also known as \cite{3205597} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)",

}

Genetic Programming entries for Matthew Andres Moreno Wolfgang Banzhaf Charles Ofria

Citations