By Roland Ewald
To choose the main appropriate simulation set of rules for a given job is usually tough. this can be as a result of problematic interactions among version gains, implementation info, and runtime surroundings, which could strongly impact the general functionality. an automatic choice of simulation algorithms helps clients in constructing simulation experiments with no hard professional wisdom on simulation. Roland Ewald analyzes and discusses current methods 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 do something about 3 events: no previous wisdom is accessible, the effect of challenge good points on simulator functionality is unknown, and a dating among challenge positive aspects and set of rules functionality will be tested empirically. the writer concludes with an experimental overview of the built equipment.
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Additional info for Automatic Algorithm Selection for Complex Simulation Problems
A function that predicts the algorithm’s performance for new problems that exhibit similar features. 11 (p. 31). We search for some parameters p ∈ Rk , with which S can be adjusted so that it exhibits a minimal maximum error when compared to the maximal performance (see def. 7, p. 2 (p. , to construct it from individual performance approximation functions per f a for all a ∈ A0 . 14 is not concerned with the speciﬁc parameters of S anymore, but with the parameters p = (pT1 , . . , pTk )T , with k = |A0 | and pi ∈ Rki .
A constant selection mapping, that works better on a given problem set. Consequently, a comparison with a random selection mapping simplicity, SC (A) is often written as SC ; it is usually clear (from the context) to which algorithm set A it refers. 4 For 28 2 Algorithm Selection as well as the best constant selection mapping is useful to asses the effectiveness of a BSMP solution. It is easy to show that all adaptive-effective selection mappings are also average-effective, but not vice versa (see previous example and proof on p.
SC∗ = argmax per f (S, P) S∈SC This gain quantiﬁes the performance difference between the optimal selection mapping and the best constant selection mapping. The larger the maximal adaptation gain, the larger the potential performance beneﬁts of selecting a suitable simulation algorithm by considering problem features. 9 deﬁnes the maximal gain to be expected when using the best constant selection mapping. 9 (Maximal Constant Gain) The maximal constant gain for a given BSMP is deﬁned as |A0 | · per f (SC∗ , P) ∑a∈A0 per f (Sa , P) where SC∗ is again the best constant selection mapping (see def.