ASlib: A Benchmark Library for Algorithm Selection (1506.02465v3)

Abstract: The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

• The paper presents a standardized benchmark library that enables consistent performance comparisons across diverse algorithm selection methods.
• It details an extensive experimental evaluation framework that supports competitive algorithm selection tasks with empirical rigor.
• The resource promotes reproducibility and interdisciplinary applications, paving the way for adaptive and machine learning-enhanced strategies.

ASlib: A Benchmark Library for Algorithm Selection

The paper "ASlib: A Benchmark Library for Algorithm Selection" presents a structured contribution to the discipline of algorithm selection by providing a standardized library for benchmarking. Recognizing the critical importance of algorithm selection in enhancing the performance of constraint solving and machine learning tasks, this work aims to establish a unified platform that promotes fair and systematic evaluation of algorithm selection methodologies.

Overview

The manuscript outlines the development and application of the Algorithm Selection Library (ASlib), which serves as a repository of diverse problem instances and algorithmic performance records. This compilation facilitates empirical assessments and advances the competitive analysis of algorithms. By offering a comprehensive experimental evaluation on library data, the paper distinguishes itself as a foundational resource for future competitions in the algorithm selection domain.

Contributions

The ASlib framework is notable for several dimensions of contribution:

• Standardization: It provides a uniform environment for comparing algorithm selection approaches, thus addressing the variability and inconsistencies prevalent in existing individual evaluation efforts.
• Resource for Competitions: The library acts as a "blueprint" for designing competition tasks, allowing researchers to benchmark their methods against established datasets and performance metrics.
• Expansion of Experimental Analysis: The paper extensively elaborates on the experimental evaluation component, thereby supplying critical insights into the effectiveness of various approaches.

Implications and Future Directions

The introduction of ASlib holds significant implications for both theoretical research and practical applications. By codifying a set of benchmarks, this work paves the way for more rigorous comparisons and improvements in algorithm selection techniques. The transparency and repeatability of experiments facilitated by ASlib can accelerate progress in the field, making the results more reproducible and comparable across different contexts and competitions.

Looking forward, the establishment of such a benchmark library invites further exploration into:

• Dynamic and Adaptive Algorithm Selection: How algorithm selection strategies can be tailored in real-time to adapt to changing input characteristics and system constraints.
• Interdisciplinary Applications: The potential to extend ASlib’s utility beyond traditional computer science applications to areas such as operations research and bioinformatics where algorithm selection remains a critical challenge.
• Integration with Machine Learning Algorithms: Investigating the synergies between machine learning models and algorithm selection processes to optimize performance in hybrid systems.

The ASlib project encapsulates a systematic effort to elevate the standards of algorithm selection research through harmonized benchmarking. As a community resource, it is positioned to significantly impact how algorithm selection is approached, evaluated, and advanced in both theoretical and application-driven contexts.