Applying natural evolution for solving computational problems - Lecture 2

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Daniel Lanza Garcia",
  title =        "Applying natural evolution for solving computational
                 problems - Lecture 2",
  booktitle =    "Inverted CERN School of Computing 2017",
  year =         "2017",
  month =        "8 " # mar,
  keywords =     "genetic algorithms, genetic programming, inverted
  bibsource =    "OAI-PMH server at",
  identifier =   "",
  language =     "eng",
  oai =          "",
  URL =          "",
  size =         "54 minutes",
  abstract =     "Darwin's natural evolution theory has inspired
                 computer scientists for solving computational problems.
                 In a similar way to how humans and animals have evolved
                 along millions of years, computational problems can be
                 solved by evolving a population of solutions through
                 generations until a good solution is found. In the
                 first lecture, the fundaments of evolutionary computing
                 (EC) will be described, covering the different phases
                 that the evolutionary process implies. ECJ, a framework
                 for researching in such field, will be also explained.
                 In the second lecture, genetic programming (GP) will be
                 covered. GP is a sub-field of EC where solutions are
                 actual computational programs represented by trees.
                 Bloat control and distributed evaluation will be
  notes =        "2017-03-08. - Streaming video 0:53:34, Lanza Garcia,
                 Daniel (speaker) (CERN, Switzerland)",

Genetic Programming entries for Daniel Lanza Garcia