Study on Architecture of Genetic Network Programming based on Cooperative Division Strategy

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Yang Yang",
  title =        "Study on Architecture of Genetic Network Programming
                 based on Cooperative Division Strategy",
  school =       "Waseda University",
  year =         "2012",
  address =      "Japan",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming, coevolution",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "113 pages",
  abstract =     "Making computers automatically solve problems is
                 central to Artificial Intelligent and many technologies
                 have been developed under the name of what is called
                 machine learning. Recently developed Genetic Network
                 Programming (GNP) is a graph-based evolutionary
                 algorithm extended from GA and GP. Because GNP
                 represents its solutions using graph structures, which
                 contributes to creating quite compact programs and
                 realising partially observable process, it has extended
                 from purely theoretical concept to real-life
                 applications in a very short time. However, the drive
                 for applying GNP to a wider range of applications
                 should be continued constantly examining the current
                 GNP architectures and making improvements. Many studies
                 have shown that real world problems have hurdles that
                 could not be solved by original GNP. Like many other
                 areas of computer science, GNP can evolve more rapidly
                 and produce better performances with new techniques.
                 The development of advanced techniques to boost GNP
                 performances seems important and promising.

                 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

                 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
  abstract =     "Chapter 2 proposes a methodology to construct the
                 models for creating trading rules using Genetic Network
                 Programming-Sarsa with Subroutines (GNPsb-Sarsa), which
                 offers an alternative population, where individuals are
                 represented by subroutines. The basic idea of GNP
                 sb-Sarsa is to automatically discover effective
                 subroutines, which capture the underlying structure and
                 building blocks of the problems. Then, the main program
                 of GNPsb-Sarsa can re-use the subroutine to enable a
                 faster evolution and even better performances.
                 GNPsb-Sarsa containing a main program and a subroutine
                 evolves by natural selection and genetic operations,
                 where the gene of GNPsb-Sarsa is the pair of the main
                 GNP and its subroutine. That is, the genetic operations
                 on GNPsb-Sarsa are constrained by the gene structure on
                 which they can operate. The following two experiments
                 are discussed: 1) Testing one subroutine node in the
                 main GNP 2) Varying the number of subroutine nodes in
                 the main GNP. The results of simulations showed that
                 the proposed GNP sb-Sarsa can provide reasonable
                 opportunities for evolving complex solutions.",
  abstract =     "Chapter 3 introduces a methodology to enhance the
                 generalisation ability of the stock trading models
                 based on GNP-Sarsa with multi-subroutines
                 (GNPmsb-Sarsa), which is a kind of extension of
                 GNPsb-Sarsa. The proposed method is developed for
                 discovering the nodes and node connections to realise
                 functions, and the functional distributed modular GNP
                 is developed. The important points of the subroutines
                 mechanism are as follows: First, the nodes and node
                 connections discovered in the subroutines are reused to
                 create effective trading rule s for certain function.
                 Second, the evolution can be achieved so quickly by
                 narrowing the search space with subroutines. Last, as
                 the kinds of functional subroutines increase, the
                 generalization ability is improved since more
                 generalised frequent transitions of GNP, i.e., building
                 blocks are found instead of precisely modelling the
                 training data, which leads to the over fitting problem.
                 Simulation results showed that the proposed method can
                 generate more efficient and generalised trading models
                 and obtain much higher profits.

                 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

                 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