On the statistical evaluation of algorithmic's computational experimentation with infeasible solutions

The experimental evaluation of algorithms results in a large set of data which generally do not follow a normal distribution or are not heteroscedastic. Besides, some of its entries may be missing, due to the inability of an algorithm to find a feasible solution until a time limit is met. Those char...

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Bibliographic Details
Published in:arXiv.org 2019-01
Main Author: Carvalho, Iago A
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Computation
Experimentation
Feasibility
Heuristic
Iterative methods
Normal distribution
Ranking
Rounding
Statistical analysis
Statistical tests
Online Access:Get full text
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Summary:The experimental evaluation of algorithms results in a large set of data which generally do not follow a normal distribution or are not heteroscedastic. Besides, some of its entries may be missing, due to the inability of an algorithm to find a feasible solution until a time limit is met. Those characteristics restrict the statistical evaluation of computational experiments. This work proposes a bi-objective lexicographical ranking scheme to evaluate datasets with such characteristics. The output ranking can be used as input to any desired statistical test. We used the proposed ranking scheme to assess the results obtained by the Iterative Rounding heuristic (IR). A Friedman's test and a subsequent post-hoc test carried out on the ranked data demonstrated that IR performed significantly better than the Feasibility Pump heuristic when solving 152 benchmark problems of Nonconvex Mixed-Integer Nonlinear Problems. However, is also showed that the RECIPE heuristic was significantly better than IR when solving the same benchmark problems.
ISSN:2331-8422
DOI:10.48550/arxiv.1902.00101