PSO-based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming

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

@InProceedings{Bartashevich:2018:PPSN,
  author =       "Palina Bartashevich and Illya Bakurov and 
                 Sanaz Mostaghim and Leonardo Vanneschi",
  title =        "{PSO}-based Search Rules for Aerial Swarms Against
                 Unexplored Vector Fields via Genetic Programming",
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11101",
  series =       "LNCS",
  pages =        "41--53",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Particle
                 swarm optimization, Vector fields, Semantics, EDDA",
  isbn13 =       "978-3-319-99252-5",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99253-2_4",
  abstract =     "In this paper, we study Particle Swarm Optimization
                 (PSO) as a collective search mechanism for individuals
                 (such as aerial micro-robots) which are supposed to
                 search in environments with unknown external dynamics.
                 In order to deal with the unknown disturbance, we
                 present new PSO equations which are evolved using
                 Genetic Programming (GP) with a semantically diverse
                 starting population, seeded by the Evolutionary Demes
                 Despeciation Algorithm (EDDA), that generalizes better
                 than standard GP in the presence of unknown dynamics.
                 The analysis of the evolved equations shows that with
                 only small modifications in the velocity equation, PSO
                 can achieve collective search behaviour while being
                 unaware of the dynamic external environment, mimicking
                 the zigzag upwind flights of birds towards the food
                 source.",
  notes =        "PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",
}

Genetic Programming entries for Palina Bartashevich Illya Bakurov Sanaz Mostaghim Leonardo Vanneschi

Citations