Experiential History as a Tuning Parameter for Attention

Efficient and Robust Parameter Tuning for Heuristic Algorithms

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

A Design Framework for Experiential History Games

This article presents empirical studies of a serious game focusing on various aspects of the American Civil War. We developed and deployed four distinct modules of our game for use within a fifth grade classroom in Virginia, USA. Of the first three modules deployed, only one lead to statistically significant results from pre-test to post-test. We used qualitative information from these first th...

A Methodology for Spark Parameter Tuning

Spark has been established as an attractive platform for big data analysis, since it manages to hide most of the complexities related to parallelism, fault tolerance and cluster setting from developers. However, this comes at the expense of having over 150 configurable parameters, the impact of which cannot be exhaustively examined due to the exponential amount of their combinations. The defaul...

Experiential learning as a method to enhance experiential teaching

Automated Parameter Tuning for Steering Algorithms

We propose a statistical framework and a methodology for automatically characterizing the influence that a steering algorithm’s parameters have on its performance. Our approach uses three performance criteria: the success rate of an algorithm in solving representative scenarios, the quality of the simulations solution, and the algorithm’s computational efficiency. Given an objective defined as ...