Ranking the Performance of Compiled and Interpreted Languages in Genetic Algorithms


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.

Proceedings of the 8th International Joint Conference on Computational Intelligence