Author(s):	Mark Burgess
Location	Room 104
Date	aug Sat 13	Days Raw Files
Start	11:50	First Raw Start	error-in-template
Duration	0:30:00	Offset	None
End	12:20	Last Raw End
Chapters
Total cuts_time	None min.

https://2016.pycon-au.org/schedule/99/view_talk
raw-playlist raw-mp4-playlist encoded-files-playlist mp4 svg png

assets release.pdf Linear_Genetic_Programming_in_Python_Bytecode.json
logs
Admin: episode episode list cut list raw files day marks day marks day image_files

State:	--------- borked edit encode push to queue post richard review 1 email review 2 make public tweet to-miror conf done
Locked:	clear this to unlock
Locked by:	user/process that locked.
Start:	initially scheduled time from master, adjusted to match reality
Duration:	length in hh:mm:ss
Name:	Video Title (shows in video search results)
Emails:	email(s) of the presenter(s)
Released:	Unknown Yes No has someone authorised pubication
Normalise:
Channelcopy:	m=mono, 01=copy left to right, 10=right to left, 00=ignore.
Thumbnail:	filename.png
Description:	A quick tutorial on genetic programming and its implementation in Python. Including the general process of evolution, the various representations of programs and particularly linear genetic programming. Some really basic python libraries are introduced, including the DEAP and pyevolve frameworks. It has been presented and prototyped by github user mgard (and documented http://multigrad.blogspot.com.au/2014/06/fun-with-python-bytecode.html) that python bytecode itself could be directly evolved in linear genetic programming to alleviate the overhead of compiling the permutations of trees generated by the evolutionary process. Python bytecode itself is redly accessed and created by Cpython, though not compatible between versions and a subset of python bytecode instructions will be selected for the implementation of a basic example of symbolic regression. A futher and more useful example will be provided. markdown
Comment:	production notes