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This thesis studies the following topics related to the architecture and applications of GNP:
Search space is a key issue while complementing GNP in the area of stock markets. There are many indexes to be considered in the stock markets, moreover their relationships are nonlinear.
Overfitting generally exists in the machine learning field, which means only specific situations can be handled instead of generalised situations. The possibility of over fitting exists because a model is typically trained using training data by maximising its performances. However, its overall performances are determined not by its performances on the training data but by its ability to perform on unseen data.
Computational complexity is also needed to be considered, since most machine learning approaches only use a single monolithic system to solve large and complex problems.
Multitask is a common feature in many real-world problems, but the standard methodology in machine learning is to study them by one system.
In order to improve the performance of GNP, when faced with the above-mentioned hurdles, this thesis introduces new advanced techniques of GNP. Firstly, hierarchical architecture GNP is proposed, which uses subroutine mechanism, and furthermore the functional subroutines are introduced. Secondly, the division architecture GNP is proposed, which uses cooperative coevolution for the subprograms. Next, the distributed architecture GNP is proposed, which has task program using multitask learning. To illustrate the performances of the proposed methods, two real-world applications are conducted. One is the stock markets, which is one of the targets for most popular investments due to its high expected profits. The second application is the tile-world, where its aim is to find successive optimal behaviours for the multi-tasks making judgements and taking proper actions for the current environments.
In chapter 1, the research background, objective and outline of the thesis are descried. The objective of this research is to propose the advanced techniques into GNP to develop better methods for real-world applications.",
Chapter 4 introduces a methodology to enhance the generalisation of the stock trading models based on Cooperative Coevolutionary Genetic Network Programming-Sarsa (GNPcc-Sarsa). The basic idea comes from both natural and artificial systems, which show that an integrated system consisting of several subsystems can reduce the total complexity of the system and solve a difficult problem satisfactorily. Therefore, a cooperative coevolution approach is proposed, where several species simultaneously evolve. Such an approach allows different species of the GNP-Sarsa model to evolve in a parallel and cooperative manner, which makes the generated model more robust, generalised and efficient for generating stock trading strategies. GNPcc-Sarsa places as few restrictions as possible to the structure, allowing the model to obtain a wide variety of architectures during the evolution and to be easily used to solve complicated problems. It has been found from simulations that the performances of the proposed model are better than those of other methods.
Chapter 5 introduces a methodology to simultaneously learn several tasks based on GNP, which is called GNP with multitasks (GNPmt), where each GNP among several GNPs corresponding to several tasks is used to learn its own task. GNPmt has some features, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results on the self-sufficient collecting problem are given to illustrate that GNPmt can give BibTeX entry too long. Truncated
Genetic Programming entries for Yang Yang