MATILDA is an online tool which makes available our new methodology known as Instance Space Analysis. It provides:

  • visualisations of the instance space for a problem
    • showing the location of benchmark test instances across the instance space
    • showing the strengths (“footprints”) and weaknesses of algorithms across the instance space
    • summarising the properties of the instances that an algorithm finds easy or hard
  • objective (unbiased) metrics of algorithmic power via footprint analysis
  • synthetic generation of new test instances at specific locations in the instance space (e.g. real-world-like instances, or instances with controllable properties)
  • automated algorithm selection tools to assist deployment of the best algorithm for a given instance.

Our efforts thus far have created instance space analyses for the following problem classes:

  1. Optimisation
    • Combinatorial Optimisation Problems
      • Graph Colouring
      • Travelling Salesman Problem
      • Knapsack Problem
      • Timetabling
    • Mathematical Programming
      • Linear Programming
      • Mixed Integer Programming
    • Continuous Optimisation
      • Black-box Single Objective
      • Black-box Multi-Objective
      • Job Shop Scheduling
  2. Learning and Model Fitting
    • Machine Learning
      • Classification
      • Regression
      • Clustering
    • Time Series Analysis
      • Time Series Forecasting
    • Image Analysis
      • Facial Age Estimation

If you have additional problems you would like to make available in MATILDA, please contact us (matilda-team@unimelb.edu.au)

VIDEO TUTORIAL: Introduction to Instance Space Analysis

USING MATILDA

The engine behind MATILDA is powered by MATLAB code, also available to download and run offline. MATILDA's online platform enables researchers to:

Use of MATILDA can be cited as:
Smith-Miles, K., Muñoz, M.A., Neelofar, 2020. Melbourne Algorithm Test Instance Library with Data Analytics (MATILDA). Available at https://matilda.unimelb.edu.au.

VIDEO TUTORIAL: How to Perform an Instance Space Analysis

Research Publications

The methodology used by MATILDA for visualizing and understanding the strengths and weaknesses of different algorithms is summarised in the following paper:

The ISA methodology was developed through a series of three papers focusing on graph colouring as a case study:

The early ideas for the instance space analysis methodology are summarised in three earlier papers:

Additional publications from the MATILDA team have applied this methodology to a wide variety of other problem domains, including: