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This paper describes a decision-making model for creating trading rules on stock markets using a graph-based evolutionary algorithm named Genetic Network Programming-Sarsa (GNP-Sarsa) and multi-subroutines. The method is developed for discovering the repetitive subgraphs over the entire graph structure and modularizing them as subroutines, which results in substantially fastening the search by suppressing...
The purpose of this paper is to study how to improve the evolution of GNP-Sarsa with subroutines and its application to trading rules on stock markets. Recently, a successful study, namely GNP-Sarsa, shows us its effectiveness and powerfulness, which combines sophisticated diversified search ability for structures using evolution and intensified search ability of RL for many technical indices and...
The purpose of this paper is to enhance the performance of Genetic Network Programming (GNP) to be used for creating trading rules on stocks, where a new method named GNP with Subroutines has been proposed. Compared to the conversional GNP, a new kind of node named subroutines node is added, which can call subprograms (subroutines) from GNP main programs. This reusable subroutines, which has judgment...
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