Evolution Evolves with Autoconstruction

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Spector:2016:GECCOcompA,

• author = "Lee Spector and Nicholas Freitag McPhee and Thomas Helmuth and Maggie M. Casale and Julian Oks",
• title = "Evolution Evolves with Autoconstruction",
• booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
• year = "2016",
• editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
• isbn13 = "978-1-4503-4323-7",
• pages = "1349--1356",
• address = "Denver, Colorado, USA",
• month = "20-24 " # jul,
• keywords = "genetic algorithms, genetic programming",
• organisation = "SIGEVO",
• DOI = "doi:10.1145/2908961.2931727",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In autoconstructive evolutionary algorithms, individuals implement not only candidate solutions to specified computational problems, but also their own methods for variation of offspring. This makes it possible for the variation methods to themselves evolve, which could, in principle, produce a system with an enhanced capacity for adaptation and superior problem solving power. Prior work on autoconsruction has explored a range of system designs and their evolutionary dynamics, but it has not solved hard problems. Here we describe a new approach that can indeed solve at least some hard problems. We present the key components of this approach, including the use of linear genomes for hierarchically structured programs, a diversity-maintaining parent selection algorithm, and the enforcement of diversification constraints on offspring. We describe a software synthesis benchmark problem that our new approach can solve, and we present visualizations of data from single successful runs of autoconstructive vs. non-autoconstructive systems on this problem. While anecdotal, the data suggests that variation methods, and therefore significant aspects of the evolutionary process, evolve over the course of the autoconstructive runs.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for Lee Spector Nicholas Freitag McPhee Thomas Helmuth Maggie M Casale Julian Oks

Citations