- author = "William B. Langdon",
- title = "Failed Disruption Propagation in Integer Genetic Programming",
- booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
- year = "2022",
- editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson",
- pages = "574--577",
- address = "Boston, USA",
- series = "GECCO '22",
- month = "9-13 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, SBSE, information loss, information funnels, entropy, evolvability, mutational robustness, optimal test oracle placement, neutral networks, software robustness, correctness attraction, diversity, software testing, theory of bloat, introns, CS.NE, CS.AI, CS.IT/math.IT, failed disruption propagation, FDP:Poster",
- isbn13 = "978-1-4503-9268-6/22/07",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2022_GECCOcomp.pdf",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2022_GECCOcomp_11pages.pdf",
-
URL = "
https://arxiv.org/abs/2204.13997",
-
DOI = "
doi:10.1145/3520304.3528878",
- acmid = "12519.33",
- size = "4 pages",
- abstract = "We inject a random value into the evaluation of highly evolved deep integer GP trees 9743720 times and find 99.7 percent Suggesting crossover and mutation impact are dissipated and seldom propagate outside the program. Indeed only errors near the root node have impact and disruption falls exponentially with depth at between exp(-depth/3) and exp(-depth/5) for recursive Fibonacci GP trees, allowing five to seven levels of nesting between the runtime perturbation and an optimal test oracle for it to detect most errors. Information theory explains this locally flat fitness landscape is due to FDP. Overflow is not important and instead, integer GP, like deep symbolic regression floating point GP and software in general, is not fragile, is robust, is not chaotic and suffers little from Lorenz butterfly.",
-
notes = "ArXiv version 11 pages.
Poster: http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/langdon_2022_GECCOcomp_poster.pdf
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
