By Roland Ewald
To choose the main compatible simulation set of rules for a given job is usually tricky. this is often because of difficult interactions among version beneficial properties, implementation information, and runtime atmosphere, which can strongly impact the final functionality. an automatic collection of simulation algorithms helps clients in establishing simulation experiments with no challenging professional wisdom on simulation. Roland Ewald analyzes and discusses current techniques to unravel the set of rules choice challenge within the context of simulation. He introduces a framework for computerized simulation set of rules choice and describes its integration into the open-source modelling and simulation framework James II. Its choice mechanisms may be able to deal with 3 events: no earlier wisdom is on the market, the influence of challenge positive factors on simulator functionality is unknown, and a dating among challenge gains and set of rules functionality may be confirmed empirically. the writer concludes with an experimental overview of the constructed tools.
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Extra info for Automatic Algorithm Selection for Complex Simulation Problems
Using this approach for algorithm selection would mean to choose the algorithm with smallest best-, worst-, or average-case f (n) for a given input size n. Whether to consider memory or time consumption would depend on the user criteria. Additionally, one might derive some estimates of the constants c and c for each algorithm. , F = N ⊂ R1 ) for selecting the most suitable algorithm. The example illustrates how the greatest strengths of complexity theory — abstractness and generality — also lead to several problems in the ASP context: Firstly, two algorithms may have similar asymptotic behavior but still perform very different, depending on the problem at hand and their speciﬁc implementation .
4 ASP in a Simulation Context To make use of the theoretical framework described in this section, it is necessary to map it onto the actual problem to be solved here, which is the selection of algorithms for simulation. Hence, A is a set of simulation algorithms that take a model as input and compute the model’s behavior. The problem space P consists of all possible models that can be simulated with the algorithms from A — but this is not sufﬁcient: as the algorithms can be executed on various computational resources that may strongly inﬂuence their performance, resource information is also relevant for algorithm selection and is therefore part of the problem space as well.
For example, such a sequence could be the set of polynomial functions with degree n = 1, 2, . . and therefore S1 ⊂ S2 ⊂ . . ⊂ S. The robustness of a selection mapping is a statistical concept that quantiﬁes how much its performance deteriorates when it is applied to unusual selection problems, or when it has been constructed by considering those . Rice illustrates this by comparing arithmetic mean and median, the latter being much more robust than the former when outliers are included [272, p.