no_starch_sampler_learn_you_some_code.pdf

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No Starch PreSS
r ea d s a mp l e c h a p t e r s f r o m
th e se n o star c h b o o k s !
im pr act ic al p yt h on pr o jects
Lee Vau ghan
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s er io us py t ho n
J uLi en Dan Jou
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math a dve nt u r es wit h p ython
Pe ter Farre LL
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pr ac t ica l sq l
ant hon y D eBar ros
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th e ru st p ro g ra mmin g l a nguage
steV e KLaB ni K an D Caro L ni Cho Ls ,
wi th Con tri Buti on s F rom t he rust C o m m u n it y
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p r ac t ica l bin ary a n alysi s
De nni s a nDr i ess e
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mis sio n p yt h on
sean m Cm an us
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c odin g w it h min ec ra ft
a L sw ei gart
Br e e ding gi a n t r at s w it h
gene tic algorithms
7
Genetic algorithms
are general-purpose
optimization programs designed to solve
complex problems. Invented in the 1970s,
they belong to the class of
evolutionary algorithms,
so named because they mimic the Darwinian process
of natural selection. They are especially useful when
little is known about a problem, when you’re dealing
with a nonlinear problem, or when searching for brute-
force-type solutions in a large search space. Best of all,
they are easy algorithms to grasp and implement.
In this chapter, you’ll use genetic algorithms to breed a race of super-
rats that can terrorize the world. After that, you’ll switch sides and help
James Bond crack a high-tech safe in a matter of seconds. These two proj-
ects should give you a good appreciation for the mechanics and power of
genetic algorithms.

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