Statistical Analysis of Computational Tests of Algorithms and Heuristics

Coffin, Marie; Saltzman, Matthew J.

doi:10.1287/ijoc.12.1.24.11899

Cited by 70 publications

(68 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…THE INVERSE {0,1}-KNAPSACK PROBLEM instances. The choice of 30 is based on the rule of thumb in statistics to produce good estimates (Coffin and Saltzman 2000).…”

Section: Design Of the Experimentsmentioning

confidence: 99%

Inverse multi-objective combinatorial optimization

Roland

Smet

Figueira

2013

Discrete Applied Mathematics

View full text Add to dashboard Cite

“…THE INVERSE {0,1}-KNAPSACK PROBLEM instances. The choice of 30 is based on the rule of thumb in statistics to produce good estimates (Coffin and Saltzman 2000).…”

Section: Design Of the Experimentsmentioning

confidence: 99%

Inverse multi-objective combinatorial optimization

Roland

Smet

Figueira

2013

Discrete Applied Mathematics

View full text Add to dashboard Cite

“…In order to gain insight into the performance evaluation a statistical analysis is needed, as also shown in [7,24,25]. The results of this statistical analysis were based on IBM PASW Statistics v.19, and are presented in this Section in order to improve and strengthen our experimental results.…”

Section: Statistical Analysis Of the Performance Evaluationmentioning

confidence: 99%

“…Assuming an algorithm with expected running time T (n, m) = O (g(n, m)), where g(n, m) is a function which the input length is parameterized by n, m, the estimated function O (g(n, m)) is usually referred to as the empirical complexity of the algorithm [7]. The empirical complexity of DNEPSA presented in this Section, was based on a statistical analysis carried out using IBM PASW Statistics v.19.…”

Section: Empirical Complexity Of Dnepsa Using Statistical Analysismentioning

confidence: 99%

On a dual network exterior point simplex type algorithm and its computational behavior

2012

View full text Add to dashboard Cite

Abstract. The minimum cost network flow problem, (MCNFP) constitutes a wide category of network flow problems. Recently a new dual network exterior point simplex algorithm (DNEPSA) for the MCNFP has been developed. This algorithm belongs to a special "exterior point simplex type" category. Similar to the classical dual network simplex algorithm (DNSA), this algorithm starts with a dual feasible tree-solution and after a number of iterations, it produces a solution that is both primal and dual feasible, i.e. it is optimal. However, contrary to the DNSA, the new algorithm does not always maintain a dual feasible solution. Instead, it produces tree-solutions that can be infeasible for the dual problem and at the same time infeasible for the primal problem. In this paper, we present for the first time, the mathematical proof of correctness of DNEPSA, a detailed comparative computational study of DNEPSA and DNSA on sparse and dense random problem instances, a statistical analysis of the experimental results, and finally some new results on the empirical complexity of DNEPSA. The analysis proves the superiority of DNEPSA compared to DNSA in terms of cpu time and iterations.Keywords. Network flows, minimum cost network flow problem, dual network exterior point simplex algorithm.Mathematics Subject Classification. 90C27, 65K05, 90B10, 91A90.

show abstract

“…Experimental work in the analysis of algorithms is important to many areas of computer science [9], [25], [31]. Apart from the obvious practical reason (algorithms are engineered to solve real-world problems), the evaluation and development of algorithms is driven as much by insight gained from experimentation as it is by theoretical analysis.…”

Section: Current Practice In Experimental Mha Researchmentioning

confidence: 99%

A general-purpose tunable landscape generator

Gallagher

Yuan

2006

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Abstract-The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metaheuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics.In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.Index Terms-Continuous optimization, empirical algorithm analysis, estimation of distribution algorithm, test-problem generator.

show abstract

Statistical Analysis of Computational Tests of Algorithms and Heuristics

Cited by 70 publications

References 45 publications

Inverse multi-objective combinatorial optimization

Inverse multi-objective combinatorial optimization

On a dual network exterior point simplex type algorithm and its computational behavior

A general-purpose tunable landscape generator

Contact Info

Product

Resources

About