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Applying natural evolution for solving computational problems - Lecture 1

<!--HTML-->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 unti...

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Autor principal: Lanza Garcia, Daniel
Lenguaje:eng
Publicado: 2017
Materias:
Acceso en línea:http://cds.cern.ch/record/2255001
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author Lanza Garcia, Daniel
author_facet Lanza Garcia, Daniel
author_sort Lanza Garcia, Daniel
collection CERN
description <!--HTML-->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.
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institution Organización Europea para la Investigación Nuclear
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publishDate 2017
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spelling cern-22550012022-11-02T22:32:27Zhttp://cds.cern.ch/record/2255001engLanza Garcia, DanielApplying natural evolution for solving computational problems - Lecture 1Inverted CERN School of Computing 2017inverted CSC<!--HTML-->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.oai:cds.cern.ch:22550012017
spellingShingle inverted CSC
Lanza Garcia, Daniel
Applying natural evolution for solving computational problems - Lecture 1
title Applying natural evolution for solving computational problems - Lecture 1
title_full Applying natural evolution for solving computational problems - Lecture 1
title_fullStr Applying natural evolution for solving computational problems - Lecture 1
title_full_unstemmed Applying natural evolution for solving computational problems - Lecture 1
title_short Applying natural evolution for solving computational problems - Lecture 1
title_sort applying natural evolution for solving computational problems - lecture 1
topic inverted CSC
url http://cds.cern.ch/record/2255001
work_keys_str_mv AT lanzagarciadaniel applyingnaturalevolutionforsolvingcomputationalproblemslecture1
AT lanzagarciadaniel invertedcernschoolofcomputing2017