Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Using tabu search to solve the common due date early/tardy machine scheduling problem
Computers and Operations Research
An analytical comparison of optimization problem generation methodologies
Proceedings of the 30th conference on Winter simulation
How to present a paper on experimental work with algorithms
ACM SIGACT News
On Reporting Computational Experiments with Mathematical Software
ACM Transactions on Mathematical Software (TOMS)
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
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The success obtained by metaheuristic techniques in resolving combinatory problems of a real character has led to a veritable explosion in the number of studies in this field. From a scientific perspective, these new approaches must demonstrate improved performance, normally with respect to prior proposals. This involves the carrying out of a series of computational experiments and statistical tests demonstrating their validity. However, in a large number of cases and for diverse causes (a lack of data relative to the benchmark heuristic or due to not knowing the most appropriate statistical tests), some authors fail to present conclusive statistical tests that may be qualified as possessing a minimum level of rigour according to the scientific method. This paper analyses the different tests that may be considered to verify significant differences between the performance of different single criteria heuristics, as well as including a number of application examples, and a discussion about the problem of robustness.