Automatic Algorithm Selection for Complex Simulation by Roland Ewald

By Roland Ewald

To decide upon the main appropriate simulation set of rules for a given job is frequently tough. this can be because of tricky interactions among version beneficial properties, implementation info, and runtime surroundings, which could strongly have an effect on the general functionality. an automatic collection of simulation algorithms helps clients in establishing simulation experiments with out not easy specialist wisdom on simulation.

Roland Ewald analyzes and discusses present 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 take care of 3 occasions: no past wisdom is offered, the impression of challenge good points on simulator functionality is unknown, and a dating among challenge good points and set of rules functionality could be tested empirically. the writer concludes with an experimental assessment of the constructed methods.

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An early formulation of the problem was presented by Chebyshev [296, p. viii]: Given a real-valued 6 These assumptions merely serve simplicity, as they avoid the use of integrals within the objective function and reflect a more realistic setup. 1 The Algorithm Selection Problem 31 function F(x, p1 , . . , pn ), determine the parameters p = (p1 , . . , pn )T ∈ Rn so that the maximum error within an interval [a, b] is minimized: argmin max |F(x, p1 , . . 11) p∈Rn x∈[a,b] Here, the function to be approximated is f (x) = 0, so that the maximum error can be regarded as the absolute maximum value of F for a given x and parameters p.

Problem-inherent variation: if simulation models contain stochastic elements, their execution may lead to different trajectories. The performance measurements of the algorithm simulating any of these trajectories may be strongly influenced by the properties of the specific trajectory. 9 to an oscillating system, or else it remains in equilibrium.

This allows a comparison of simulation algorithms in terms of speed and solution quality in theory — but in practice, this technique is hardly effective: deciding which algorithm is better for a given input by simply trying out all of them will typically result in a large overhead; and since the solution is obtained during the process, there is no need to apply the selected best-performing algorithm to the same input again (unless stochasticity is involved, see sec. 1). Relation to Optimization The principal ASP sub-problem, the BSMP, requires searching for a best element within a given space of selection mappings S0 (see def.

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