Distributed Genetic Algorithms with an Application to Portfolio Selection Problems
This paper presents a PVM-based coarse-grained distributed genetic algorithm implemented on workstation clusters. After successfully evaluating the algorithm with standard test functions, we apply it to a hard real-world portfolio selection problem. The distributed version easily outperforms sequential genetic algorithms and shows promise for difficult management applications.
KeywordsGenetic Algorithm Portfolio Selection Efficient Frontier Portfolio Selection Problem Island Model
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