Algorithm Engineering: Concepts and Practice

Chimani, Markus; Klein, Karsten

doi:10.1007/978-3-642-02538-9_6

Cited by 12 publications

(10 citation statements)

References 48 publications

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“…Algorithm engineering as defined by Sanders [58], is a cycle of design, analysis, implementation and experimentation that leads 1 to new design ideas. As experiments play an important role in developing algorithms, algorithm engineering [16] over the past few years has become a well known definition for experimental evaluation in the context of algorithm development. In addition to that, the goal of algorithm engineering is also to speed up the process of transferring algorithmic knowledge into applications.…”

Section: Algorithm Engineeringmentioning

confidence: 99%

“…It is assumed that be-61 fore execution, T is in a buffer followed by at least 2m +14 null characters. Thereafter, the scanning is moved forward according to the next position of character Y in the text window i.e at the position t 16 . Here, another occurrence of the pattern is found.…”

Section: Algorithm 18 Lf(pmentioning

confidence: 99%

“…if i > n − m then 13: return occ − 1 14: for i ← a step b to L do 13: for j ← 0 to c − 1 do 14: When the block of m ones ends at k 1 , the shift is one and it is computed in a single loop (lines [16][17]. When the block of m ones ends at k 0 , a match candidate is found, and this is expressed as assigning a zero to all such entries (lines [18][19].…”

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confidence: 99%

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Improved online algorithms for jumbled matching

Ghuman

Tarhio

Chhabra

2020

Discrete Applied Mathematics

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Section: Algorithm Engineeringmentioning

confidence: 99%

Section: Algorithm 18 Lf(pmentioning

confidence: 99%

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Improved online algorithms for jumbled matching

Ghuman

Tarhio

Chhabra

2020

Discrete Applied Mathematics

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“…In many situations, it is the only way to advance scientific progress. In others, it can be a valuable addition to theory, as emphasized by the Algorithm Engineering approach [26]. However, it is necessary to be aware of the problems one may run into and to follow a suitable experimental methodology instead of doing ad-hoc tests.…”

Section: Performing Experiments In Computer Sciencementioning

confidence: 99%

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

Bartz-Beielstein

Preuß

2013

Natural Computing Series

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This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in computer science, or more concrete, metaheuristic optimization. The experimental setup is discussed together with the pitfalls awaiting the unexperienced (and sometimes even the experienced). We present a severity criterion as a meta-statistical concept for evaluating statistical inferences, which can be used to avoid fallacies, i.e., misconceptions resulting from incorrect reasoning in argumentation caused by floor or ceiling effects. The sequential parameter optimization is discussed as a meta-statistical framework which integrates concepts such as severity. Parameter tuning is considered as a relatively new tool in method design and analysis, and it leads to the question of adaptability of optimization algorithms. Another branch of experimentation aims for attaining more concrete problem knowledge, we may term it 'exploratory landscape analysis', containing sample and visualization techniques that are often applied but not seen as being a methodological contribution. However, this chapter is not only a renarration of well known facts. We also try a look into the future to estimate what the hot topics of methodological research will be in the next years and what changes we may expect for the whole community.

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“…Experimental comparison of algorithms has long been recognised as an essential aspect of research on meta-heuristics [2,36,47,18]. Despite well-earned criticisms of some disconnect between theory and experimentation [35,17] and a few methodological issues that still require adequate attention [24,6], experimental evaluation remains a central aspect and a major component of the development and understanding of EAs.…”

Section: Introductionmentioning

confidence: 99%

Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

Campelo¹,

Wanner²

2019

Preprint

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This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm's step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms.

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Algorithm Engineering: Concepts and Practice

Cited by 12 publications

References 48 publications

Improved online algorithms for jumbled matching

Improved online algorithms for jumbled matching

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

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