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In this paper, we present a scheme whereby diverse optimization algorithms are incorporated within a framework of selective reproduction according to fitness. By forming an ensemble of several populated optimization algorithms, it is shown that the exploitative traits can be extended across several search algorithms. Results of simulations on several difficult quadratic assignment problem benchmarks based on a fixed computational time budget have shown that the ensemble scheme convincingly outperforms the individual constituent optimization algorithms.

: Pricing and hedging of higher order derivatives such as multidimensional (up to 100 underlying assets) European and first generation exotic options represent mathematically complex and computationally intensive problems. Grid computing promises to give the capability to handle such intense computations. With several Grid middleware solutions available for gridifying traditional applications, it is cumbersome to select an ideal candidate, to develop financial applications, that can cope up with time critical computational demand for complex pricing requests. In this paper we present SuperQuant Financial Benchmark Suite to evaluate and quantify the overhead imposed by a Grid middleware on throughput of the system and turnaround times for computation. This approach is a step towards producing a middleware independent, reproducible, comparable, self-sufficient and fair performance analysis of Grid middlewares. The result of such performance analysis can be used by middleware vendors to find the bottlenecks and problems in their design and implementation of the system and by financial application developers to verify implementation of their financial algorithms. In this paper we explain the motivation and the details of the proposed benchmark suite. As a proof of concept, we utilize the benchmarks in an International Grid Programming contest and demonstrate the result of initial experiments.

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