The dynamic encoding algorithm for searches (DEAS) is a recently developed algorithm that comprises a series of global optimization methods based on variable-length binary strings that represent real variables. It has been successfully applied to various optimization problems, exhibiting outstanding search efficiency and accuracy. Because DEAS manages binary strings or matrices, the decoding rules applied to the binary strings and the algorithm's structure determine the aspects of local search. The decoding rules used thus far in DEAS have some drawbacks in terms of efficiency and mathematical analysis. This paper proposes a new decoding rule and applies it to univariate DEAS (uDEAS), validating its performance against several benchmark functions. The overall optimization results of the modified uDEAS indicate that it outperforms other metaheuristic methods and obviously improves upon older versions of DEAS series.