Effects of Lexicase and Tournament Selection on Diversity Recovery and Maintenance

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Helmuth:2016:GECCOcomp,

• author = "Thomas Helmuth and Nicholas Freitag McPhee and Lee Spector",
• title = "Effects of Lexicase and Tournament Selection on Diversity Recovery and Maintenance",
• 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 = "983--990",
• address = "Denver, Colorado, USA",
• month = "20-24 " # jul,
• keywords = "genetic algorithms, genetic programming",
• organisation = "SIGEVO",
• DOI = "doi:10.1145/2908961.2931657",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In genetic programming systems, parent selection algorithms select the programs from which offspring will be produced by random variation and recombination. While most parent selection algorithms select programs on the basis of aggregate performance on multiple test cases, the lexicase selection algorithm considers each test case individually, in random order, for each parent selection event. Prior work has shown that lexicase selection can produce both more diverse populations and more solutions when applied to several hard problems. Here we examine the effects of lexicase selection, compared to those of the more traditional tournament selection algorithm, on population error diversity using two program synthesis problems. We conduct experiments in which the same initial population is used to start multiple runs, each using a different random number seed. The initial populations are extracted from genetic programming runs, and fall into three categories: high diversity populations, low diversity populations, and populations that occur after diversity crashes. The reported data shows that lexicase selection can maintain high error diversity and also that it can re-diversify less-diverse populations, while tournament selection consistently produces lower diversity.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for Thomas Helmuth Nicholas Freitag McPhee Lee Spector

Citations