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This paper introduces an evolutionary approach to the variability analysis of nonlinear systems. The algorithm shows some advantages with respect to classical stochastic approaches and, in particular, with respect to Monte Carlo-based methods. First, it allows a significant reduction of the underestimation error that affects any nondeterministic approach; in other words, it guarantees a considerable saving of simulation time. Moreover, the technique ensures a higher reliability in pursuing the true range of variation of the system performance of interest. The method has been applied to a practical problem of great interest: the variability analysis of a stress control tube made of a composite material used for electric stress relief in cable accessories. The cable termination has been modeled by means of a lumped nonlinear circuit. The proposed technique also provides reliable results whenever large parameter variations need to be taken into account and/or nonlinear functions of varying parameters are considered. Simulation results prove it to be of great support for the designer in cable termination dimensioning and in composite material selection. Index Terms--Cable accessories, composite materials, variability analysis.

This paper presents an evolutionary approach to worst case tolerance design of magnetic devices. The algorithm improves on the classical nominal design, accounting for parameter variations and tolerances, so that the system performance does not exceed upper and lower specifications imposed in advance by the designer. The evolutionary algorithm adjusts central values and tolerances of parameters so that a user-defined objective function is optimized and the design constraints are met. The feasibility of the candidate sets of design parameters and tolerances is estimated by means of vertex analysis. The algorithm ensures that the optimized design set of parameters and tolerances is 100% feasible in a large number of practical eases. This paper gives an example of the design of a magnetic device and discusses the salient features and limitations of the approach. Index Terms--Genetic algorithms, tolerance analysis, tolerance design.