From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Mironovich:2018:GECCOcomp,
  author =       "Vladimir Mironovich and Maxim Buzdalov and 
                 Valeriy Vyatkin",
  title =        "From fitness landscape analysis to designing
                 evolutionary algorithms: the case study in automatic
                 generation of function block applications",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "1902--1905",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208230",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  abstract =     "Search-based software engineering, a discipline that
                 often requires finding optimal solutions, can be a
                 viable source for problems that bridge theory and
                 practice of evolutionary computation. In this research
                 we consider one such problem: generation of data
                 connections in a distributed control application
                 designed according to the IEC 61499 industry
                 standard.

                 We perform the analysis of the fitness landscape of
                 this problem and find why exactly the simplistic (1 +
                 1) evolutionary algorithm is slower than expected when
                 finding an optimal solution to this problem. To
                 counteract, we develop a population-based algorithm
                 that explicitly maximises diversity among the
                 individuals in the population. We show that this
                 measure indeed helps to improve the running times.",
  notes =        "Also known as \cite{3208230}
                 \cite{Mironovich:2018:FLA:3205651.3208230}

                 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 Vladimir Anatolevich Mironovich Maxim Buzdalov Valerii Vladimirovich Vyatkin

Citations