Pablo García-Sánchez

Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee et al.

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

Juan J. Merelo-Guervós, Antonio Fernández-Ares, Antonio Álvarez Caballero et al.

The game Starcraft is one of the most interesting arenas to test new machine learning and computational intelligence techniques; however, StarCraft matches take a long time and creating a good dataset for training can be hard. Besides, analyzing match logs to extract the main characteristics can also be done in many different ways to the point that extracting and processing data itself can take an inordinate amount of time and of course, depending on what you choose, can bias learning algorithms. In this paper we present a simplified dataset extracted from the set of matches published by Robinson and Watson, which we have called RedDwarfData, containing several thousand matches processed to frames, so that temporal studies can also be undertaken. This dataset is available from GitHub under a free license. An initial analysis and appraisal of these matches is also made.

Juan-J. Merelo, Mario García-Valdez, Pedro A. Castillo et al.

JavaScript is an interpreted language mainly known for its inclusion in web browsers, making them a container for rich Internet based applications. This has inspired its use, for a long time, as a tool for evolutionary algorithms, mainly so in browser-based volunteer computing environments. Several libraries have also been published so far and are in use. However, the last years have seen a resurgence of interest in the language, becoming one of the most popular and thus spawning the improvement of its implementations, which are now the foundation of many new client-server applications. We present such an application for running distributed volunteer-based evolutionary algorithm experiments, and we make a series of measurements to establish the speed of JavaScript in evolutionary algorithms that can serve as a baseline for comparison with other distributed computing experiments. These experiments use different integer and floating point problems, and prove that the speed of JavaScript is actually competitive with other languages commonly used by the evolutionary algorithm practitioner.

Juan-J. Merelo, Pablo García-Sánchez, Mario García-Valdez et al.

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.

Juan Julián Merelo-Guervós, Pablo García-Sánchez

From the era of big science we are back to the "do it yourself", where you do not have any money to buy clusters or subscribe to grids but still have algorithms that crave many computing nodes and need them to measure scalability. Fortunately, this coincides with the era of big data, cloud computing, and browsers that include JavaScript virtual machines. Those are the reasons why this paper will focus on two different aspects of volunteer or freeriding computing: first, the pragmatic: where to find those resources, which ones can be used, what kind of support you have to give them; and then, the theoretical: how evolutionary algorithms can be adapted to an environment in which nodes come and go, have different computing capabilities and operate in complete asynchrony of each other. We will examine the setup needed to create a very simple distributed evolutionary algorithm using JavaScript and then find a model of how users react to it by collecting data from several experiments featuring different classical benchmark functions.