A poster presented at ECTA/IJCCI 2016 with our research on evolutionary algorithms. Paper sources and data at https://github.com/geneura-papers/2016-ea-languages-PPSN/releases/tag/v1.0ECTA
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Benchmarking languages for evolutionary computation
1. This work has been supported in part by
projects TIN2014-56494-C4-3-P (Spanish
Ministry of Economy and
Competitiveness) and project V17-2015 of
the Microprojects program 2015 from CEI
BioTIC Granada.
Image credits
● Background: N. Raymond at flic.kr/p/h6R8go
● Cars: CarSpotter at flic.kr/p/d1kZ3J
● Language logos from Wikipedia
● Winners from goo.gl/BeLv0H by Chris
McDonald
Ranking the performance ofRanking the performance of
compiled and interpretedcompiled and interpreted
languages in geneticlanguages in genetic
algorithmsalgorithms
“10110011001100” (T,F,T,T,F,F,T,T,F,F,T,T,F,F)
Compiled languages are best.
Python is fast.
Perl and Node are fast.
Data structures don't matter.
Check out Rust and Go!