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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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Lenguaje: | eng |
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2017
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Acceso en línea: | http://cds.cern.ch/record/2255001 |
_version_ | 1780953680535617536 |
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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. |
id | cern-2255001 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
record_format | invenio |
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 |