# Applying natural evolution for solving computational problems - Lecture 2

- Institution: CERN
- Department: inverted CSC
- Speaker: Daniel Lanza Garcia
- Year: 2017
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3016227)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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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## Lectures

1. [Applying natural evolution for solving computational problems - Lecture 2](https://invisiblenetwork.ai/learn/cern/3016227/7730b0bc7216), 54 min (CERN's player)