Benchmarking languages for evolutionary computation
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The document discusses the performance ranking of compiled and interpreted programming languages in genetic algorithms, highlighting that compiled languages generally perform better, with Python also noted for its speed. It mentions contributions from various projects and image credits. Additionally, it suggests exploring Rust and Go for their performance profiles.
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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!