abstract
- When multiple algorithms are available to solve a particular problem, deciding which to use may be challenging. Depending on the problem at hand and the available algorithms, choosing the wrong solver might represent significant performance losses. Machine learning has emerged as a paramount tool for implementing algorithm selectors. Regardless of their success in various applications, how incomplete information on the individual solvers¿ performance may affect the overall quality of the decisions remains unexplored. This work uses Neural Networks (multi-layer perceptron) to implement algorithm selectors for the one-dimensional bin packing problem. We explore situations involving missing values and their treatment to observe their impact on the algorithm selectors generated. Our results suggest that the algorithm selectors may be more sensitive to missing values than expected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.