Israel Blancas

In this paper we measure the speed of several popular and recent programming languages performing the most usual operators in the canonical evolutionary algorithm, mutation and crossover, as well as an usual fitness function, OneMax. These three operations are representative of the kind of the ones performed in binary chromosomes. Our main objectives are, first, to create programs in programming languages that use the fastest available implementation. Second, to find out the differences in… Mostrar más

In this paper we measure the speed of several popular and recent programming languages performing the most usual operators in the canonical evolutionary algorithm, mutation and crossover, as well as an usual fitness function, OneMax. These three operations are representative of the kind of the ones performed in binary chromosomes. Our main objectives are, first, to create programs in programming languages that use the fastest available implementation. Second, to find out the differences in speeds for the different languages. Third, to find out whether the usual assumptions about the speed of languages really holds. And, finally, to find if the assumed order of speed in languages used in evolutionary algorithms holds true for all kinds of operations. In order to do that, we use available implementations or perform our own, concluding that the evolutionary algorithm scenario is more complex than usually assumed and finding out some surprising winners and losers among the languages tested. Mostrar menos

Although performance is important, several other issues should be taken into account when choosing a particular language for implementing an evolutionary algorithm, such as the fact that the speed of different languages when carrying out an operation will depend on several factors, including the size of the operands, the version of the language and underlying factors such as the operating system. However, it is usual to rely on compiled languages, namely Java or C/C++, for carrying out any… Mostrar más

Although performance is important, several other issues should be taken into account when choosing a particular language for implementing an evolutionary algorithm, such as the fact that the speed of different languages when carrying out an operation will depend on several factors, including the size of the operands, the version of the language and underlying factors such as the operating system. However, it is usual to rely on compiled languages, namely Java or C/C++, for carrying out any implementation without considering other languages or rejecting them outright on the basis of performance. Since there are a myriad of languages nowadays, it is interesting however to measure their speed when performing operations that are usual in evolutionary algorithms. That is why in this paper we have chosen three evolutionary algorithm operations: bitflip mutation, crossover and the fitness function OneMax evaluation, and measured the speed for several popular, and some not so popular, languages. Our measures confirm that, in fact, Java, C and C++ not only are the fastest, but also have a behaviour that is independent of the size of the chromosome. However, we have found other compiled language such as Go or interpreted languages such as Python to be fast enough for most purposes. Besides, these experiments show which of these measures are, in fact, the best for choosing an implementation language based on its performance. Mostrar menos

La computación voluntaria es un tipo de computación distribuida que permite
realizar experimentos utilizando la potencia de cálculo proporcionada
por las máquinas de usuarios que deciden colaborar libremente en la investigación.
NodIO es un marco de desarrollo de aplicaciones para la creación de este
tipo de experimentos. A diferencia de otros marcos de desarrollo que realizan las mismas
tareas, NodIO no necesita instalar ninguna aplicación ni biblioteca en
el lado del… Mostrar más

La computación voluntaria es un tipo de computación distribuida que permite
realizar experimentos utilizando la potencia de cálculo proporcionada
por las máquinas de usuarios que deciden colaborar libremente en la investigación.
NodIO es un marco de desarrollo de aplicaciones para la creación de este
tipo de experimentos. A diferencia de otros marcos de desarrollo que realizan las mismas
tareas, NodIO no necesita instalar ninguna aplicación ni biblioteca en
el lado del cliente, ya que se
encuentra escrito en JavaScript, por lo que solo será necesario
tener un navegador web o un intérprete.
En este trabajo se detallará el funcionamiento de NodIO,
además de mostrar dos experimentos para demostrar los beneficios
que exponemos: sencillez y rendimiento.
Ambos experimentos nos demostrarán cómo, al aumentar
el número de "voluntarios'', el tiempo en alcanzar una solución
desciende. Mostrar menos

Despite the existence and popularity of many new and classical computer languages, the evolu- tionary algorithm community has mostly exploited a few popular ones, avoiding them, especially if they are not compiled, under the asumption that compiled languages are always faster than interpreted languages. Wide-ranging performance analyses of implementation of evolutionary al- gorithms are usually focused on algorithmic implementation details and data structures, but these are usually limited to… Mostrar más

Despite the existence and popularity of many new and classical computer languages, the evolu- tionary algorithm community has mostly exploited a few popular ones, avoiding them, especially if they are not compiled, under the asumption that compiled languages are always faster than interpreted languages. Wide-ranging performance analyses of implementation of evolutionary al- gorithms are usually focused on algorithmic implementation details and data structures, but these are usually limited to specific languages. In this paper we measure the execution speed of three common operations in genetic algorithms in many popular and emerging computer languages us- ing different data structures and implementation alternatives, with several objectives: create a ranking for these operations, compare relative speeds taking into account different chromosome sizes and data structures, and dispel or show evidence for several hypotheses that underlie most popular evolutionary algorithm libraries and a pplications. We find that there is indeed basis to consider compiled languages, such as Java, faster in a general sense, but there are other languages, including interpreted ones, that can hold its ground against them. Mostrar menos

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… Mostrar más

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. Mostrar menos

Creating rankings might seem like a vain exercise in belly-button gazing, even more so for people so unlike that kind of things as programmers. However, in this paper we will try to prove how creating city (or province) based rankings in Spain has led to all kind of interesting effects, including increased productivity and community building. We describe the methodology we have used to search for programmers residing in a particular province focusing on those where most population is… Mostrar más

Creating rankings might seem like a vain exercise in belly-button gazing, even more so for people so unlike that kind of things as programmers. However, in this paper we will try to prove how creating city (or province) based rankings in Spain has led to all kind of interesting effects, including increased productivity and community building. We describe the methodology we have used to search for programmers residing in a particular province focusing on those where most population is concentrated and apply different measures to show how these communities differ in structure, number and productivity. Mostrar menos

Abstract:
It is not usual practice in the evolutionary algorithms area to benchmark different operations in order to choose the best language for a single or multilanguage implementation. Researchers rely instead on common practice or frameworks using mainstream languages. That is why it is usual practice to choose compiled languages (namely Java or C/C++) when implementing evolutionary algorithms, without considering other languages or rejecting them outright on the basis of performance… Mostrar más

Abstract:
It is not usual practice in the evolutionary algorithms area to benchmark different operations in order to choose the best language for a single or multilanguage implementation. Researchers rely instead on common practice or frameworks using mainstream languages. That is why it is usual practice to choose compiled languages (namely Java or C/C++) when implementing evolutionary algorithms, without considering other languages or rejecting them outright on the basis of performance. Since there is a myriad of languages nowadays, we considered it an interesting challenge to measure their speed when performing frequent operations in evolutionary algorithms. In this paper we have tested three basic evolutionary algorithm operations over binary chromosomes: bitflip mutation, crossover and the OneMax fitness function. As a performance measure, the speed for both popular and not so popular computer languages have been used. In general, the results confirm that compiled languages scale and perform better, but also in some cases have a behaviour that is independent of the size of the chromosome. Additionally, results show that other languages, such as Go (compiled) or Python (interpreted) are fast enough for most purposes. Besides, these experiments show which of these operations are, in fact, the best for choosing an implementation language based on its performance. Mostrar menos