Swarm-based metaheuristics in automatic programming: a survey

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Juan L. Olmo and Jose R. Romero and 
                 Sebastian Ventura",
  title =        "Swarm-based metaheuristics in automatic programming: a
  journal =      "Wiley Interdisciplinary Reviews: Data Mining and
                 Knowledge Discovery",
  year =         "2014",
  volume =       "4",
  number =       "6",
  pages =        "445--469",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Wiley Periodicals, Inc",
  ISSN =         "1942-4795",
  URL =          "http://dx.doi.org/10.1002/widm.1138",
  DOI =          "doi:10.1002/widm.1138",
  abstract =     "On the one hand, swarm intelligence (SI) is an
                 emerging field of artificial intelligence that takes
                 inspiration in the collective and social behaviour of
                 different groups of simple agents. On the other hand,
                 the automatic evolution of programs is an active
                 research area that has attracted a lot of interest and
                 has been mostly promoted by the genetic programming
                 paradigm. The main objective is to find computer
                 programs from a high-level problem statement of what
                 needs to be done, without needing to know the structure
                 of the solution beforehand. This paper looks at the
                 intersection between SI and automatic programming,
                 providing a survey on the state-of-the-art of the
                 automatic programming algorithms that use an SI
                 metaheuristic as the search technique. The expression
                 of swarm programming (SP) has been coined to cover
                 swarm-based automatic programming proposals, since they
                 have been published to date in a disorganised manner.
                 Open issues for future research are listed. Although it
                 is a very recent area, we hope that this work will
                 stimulate the interest of the research community in the
                 development of new SP metaheuristics, algorithms, and

Genetic Programming entries for Juan Luis Olmo Jose Raul Romero Salguero Sebastian Ventura