A general CPL-AdS methodology for fixing dynamic parameters in dual environments. 
The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems.  However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable.  On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster.  This paper presents a generalized universal decision formula to solve this bottleneck problem.  As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model.  Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ in both informative and deceptive environments.  Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method.  All of these aforementioned contributions are vitally important whether in theory or in practical applications.  Finally, extensive experiments show that our proposed approaches are efficient and feasible.