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PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS
Abstract A survey of two parallel evolutionary computation techniques is presented: the genetic algorithms and genetic programming methods. An application of this approach to the induction of trading models is presented for financial assets, which is known as a hard problem. This study analyses the potential of this approach and the benefit of parallelization.
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