Evolving event-driven programs with SignalGP

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Alexander Lalejini and Charles Ofria",
  title =        "Evolving event-driven programs with {SignalGP}",
  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",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1135--1142",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205523",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present SignalGP, a new genetic programming (GP)
                 technique designed to incorporate the event-driven
                 programming paradigm into computational evolution's
                 toolbox. Event-driven programming is a software design
                 philosophy that simplifies the development of reactive
                 programs by automatically triggering program modules
                 (event-handlers) in response to external events, such
                 as signals from the environment or messages from other
                 programs. SignalGP incorporates these concepts by
                 extending existing tag-based referencing techniques
                 into an event-driven context. Both events and functions
                 are labelled with evolvable tags; when an event occurs,
                 the function with the closest matching tag is
                 triggered. In this work, we apply SignalGP in the
                 context of linear GP. We demonstrate the value of the
                 event-driven paradigm using two distinct test problems
                 (an environment coordination problem and a distributed
                 leader election problem) by comparing SignalGP to
                 variants that are otherwise identical, but must
                 actively use sensors to process events or messages. In
                 each of these problems, rapid interaction with the
                 environment or other agents is critical for maximizing
                 fitness. We also discuss ways in which SignalGP can be
                 generalized beyond our linear GP implementation.",
  notes =        "Also known as \cite{3205523} 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 Alexander Lalejini Charles Ofria