Applying natural evolution for solving computational problems - Lecture 2

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Misc{oai:cds.cern.ch:2255146,

• 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 csc",
• bibsource = "OAI-PMH server at cds.cern.ch",
• identifier = "oai:cds.cern.ch:2255146",
• language = "eng",
• oai = "oai:cds.cern.ch:2255146",
• video_url = "http://cds.cern.ch/record/2255146",
• 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 introduced.",
• notes = "2017-03-08. - Streaming video 0:53:34, Lanza Garcia, Daniel (speaker) (CERN, Switzerland)",

}

Genetic Programming entries for Daniel Lanza Garcia

Citations