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Home > Applying natural evolution for solving computational problems - Lecture 2
 
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Title Applying natural evolution for solving computational problems - Lecture 2
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Author(s) Lanza Garcia, Daniel (speaker) (CERN, Switzerland)
Corporate author(s) CERN. Geneva
Imprint 2017-03-08. - Streaming video.
Series (inverted CSC)
(Inverted CERN School of Computing 2017)
Lecture note on 2017-03-08T11:30:00
Subject category inverted CSC
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.
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Submitted by catharine.noble@cern.ch

 


 Record created 2017-03-09, last modified 2022-11-02
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