The proposed procedure MAPO(Multi-Algorithm Parallel Optimization) iteratively uses a committee of serial response surface algorithms to facilitate the generation of a sizeable number of distinctly different points for costly function evaluation in parallel. We refer to the methods as algorithm-experts. This approach can be effective because the computation time for one serial response surface optimization algorithm is very small in comparison to the CPU time required to evaluate costly f(x). Hence MAPO has a committee of experts, each one of which selects several candidate points x for costly function evaluation.The two main classes of algorithm-experts use Radial Basis Functions and Neural Nets. The algorithm will be applied to a range of difficult test problems and to three classes of costly real engineering functions. Two of these applications come from the PI's own research projects on environmental pollution and safety of drinking water. Another project involves a finite element model of a mechanics system based on partial differential equations provided by the NSF ITR Adaptive Software Project at the Cornell Theory Center.
