  It is quite usual when an evolutionary algorithm tool or library uses a
language other than C, C++, Java or Matlab that a reviewer or the audience
questions its usefulness based on the speed of those other languages,
purportedly slower than the aforementioned ones. Despite speed being not
everything needed to design a useful evolutionary algorithm application, in
this paper we will measure the speed for several very basic evolutionary
algorithm operations in several languages which use different virtual machines
and approaches, and prove that, in fact, there is no big difference in speed
between interpreted and compiled languages, and that in some cases, interpreted
languages such as JavaScript or Python can be faster than compiled languages
such as Scala, making them worthy of use for evolutionary algorithm
experimentation.

Neural and Evolutionary Computing;Performance