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In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region framework (TRF). Inspired by the success of TRF in line search, here we present a Trusted Evolutionary Algorithm (TEA) which is a surrogate-assisted evolutionary algorithm that exhibits the concept of surrogate model trustworthiness in its search. Empirical study on benchmark functions reveals that TEA converges to near-optimum solutions more efficiently than the canonical evolutionary algorithm.