Antonio Mora García

Browser-based distributed evolutionary computation: performance and scaling behavior

The challenge of ad-hoc computing is to find the way of taking advantage of spare cycles in an efficient way that takes into account all capabilities of the devices and inter connections available to them. In this paper we explore distributed evolutionary computation based on the Ruby on Rails framework, which overlays a Model-View-Controller on evolutionary computation. It allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in an evolutionary computation experiment. Using a straight forward farming model, we consider different factors, such as the size of the population used. We are mostly interested in how they impact on performance, but also the scaling behavior when a non-trivial number of computers is applied to the problem. Experiments show the impact of different packet sizes on performance, as well as a quite limited scaling behavior, due tothe characteristics of the server. Several solutions for that problem are proposed.

Entropy-driven evolutionary approaches to the mastermind problem

Mastermind is a well-known board game in which one player must discover a hidden color combination set up by an opponent, using the hints the latter provides (the number of places -or pegs- correctly guessed, and the number of colors rightly guessed but out of place in each move). This game has attracted much theoretical attention, since it constitutes a very interesting example of dynamically-constrained combinatorial problem, in which the set of feasible solutions changes with each combination played. We present an evolutionary approach to this problem whose main features are the seeded initialization of the population using feasible solutions discovered in the previous move, and the use of an entropy-based criterion to discern among feasible solutions. This criterion is aimed at maximizing the information that will be returned by the opponent upon playing a combination. Three variants of this approach, respectively based on the use of a single population and two cooperating or competing subpopulations are considered. It is shown that these variants achieve the playing level of previous state-of-the-art evolutionary approaches using much lower computational effort (as measured by the number of evaluations required).

Statistical analysis of parameter setting in real-coded evolutionary algorithms

When evolutionary algorithm (EA) applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of the algorithm. This paper proposes using the ANOVA (ANalysis Of the VAriance) method to carry out an exhaustive analysis of an EA method and the different parameters it requires, such as those related to the number of generations, population size, operators application and selection type. When undertaking a detailed statistical analysis of the influence of each parameter, the designer should pay attention mostly to the parameter presenting values that are statistically most significant. Following this idea, the significance and relative importance of the parameters with respect to the obtained results, as well as suitable values for each of these, were obtained using ANOVA on four well known function optimization problems.

Designing educational games: Key elements and methodological approach

Serious games, especially educational video games, have proliferated in recent years. Despite this, there are very few methodological proposals capable of addressing the design of all the key elements of an educational video game. To deal with this discrepancy, this paper presents a methodology divided into five phases: startup, design, production, test, and post-production; focusing on the design phase of educational games. The design phase structures an educational video game in acts, scenes, scenarios, actions and dialogues; and defines the modeling tasks and generated artifacts which have to be carried out in each step of the process.

Evolution of XPath lists for document data selection

XML has became a standard for structured data, and very often transformations from one specific format to another are needed. XSLT stylesheets are programs designed for this purpose, and they use XPath expressions to select sets of nodes within the document. In this paper a new version of an evolutionary algorithm that creates XSLT from examples is presented, improving on previously obtained results by testing a new individual representation with a new set of operators, based mainly on evolution of XPaths with a fixed XSLT program structure. The experiments show that this new representation, and a lower set of operators, yield better results in less generations that in our previous version.