Some models of graphs for scheduling sports competitions
Discrete Applied Mathematics
PYTHIA: a knowledge-based system to select scientific algorithms
ACM Transactions on Mathematical Software (TOMS)
New methods to color the vertices of a graph
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
An introduction to variable and feature selection
The Journal of Machine Learning Research
Extensions to metric based model selection
The Journal of Machine Learning Research
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
A graph coloring heuristic using partial solutions and a reactive tabu scheme
Computers and Operations Research
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
An improved ant colony optimisation heuristic for graph colouring
Discrete Applied Mathematics
Facet defining inequalities among graph invariants: The system GraPHedron
Discrete Applied Mathematics
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Computers and Operations Research
Learning and Intelligent Optimization
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Use of the Szeged index and the revised Szeged index for measuring network bipartivity
Discrete Applied Mathematics
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Discovering the suitability of optimisation algorithms by learning from evolved instances
Annals of Mathematics and Artificial Intelligence
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
Generalising algorithm performance in instance space: a timetabling case study
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
An algorithm for finding a maximum clique in a graph
Operations Research Letters
Communication: House of Graphs: A database of interesting graphs
Discrete Applied Mathematics
The Cunningham-Geelen Method in Practice: Branch-Decompositions and Integer Programming
INFORMS Journal on Computing
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This paper tackles the difficult but important task of objective algorithm performance assessment for optimization. Rather than reporting average performance of algorithms across a set of chosen instances, which may bias conclusions, we propose a methodology to enable the strengths and weaknesses of different optimization algorithms to be compared across a broader instance space. The results reported in a recent Computers and Operations Research paper comparing the performance of graph coloring heuristics are revisited with this new methodology to demonstrate (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; and (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively.