A Study on the Memory Schemes for Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{FengmingYe:thesis,
  author =       "Fengming Ye",
  title =        "A Study on the Memory Schemes for Genetic Network
                 Programming",
  school =       "Waseda University",
  year =         "2011",
  address =      "Japan",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming",
  URL =          "http://jairo.nii.ac.jp/0069/00021616/en",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/3/Honbun-5698.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/2/Shinsa-5698.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/37550/1/Gaiyo-5698.pdf",
  size =         "105 pages",
  abstract =     "From 1960s, Evolutionary Algorithms (EA), which is an
                 important subtopic of Artificial Intelligence (AI), has
                 been studied a lot and great progresses have been made
                 continuously to improve the existed algorithms or
                 propose novel methods. For example, the studies on many
                 classical methods such as Genetic Algorithm (GA),
                 Genetic Programming (GP), Evolutionary Strategies (ES),
                 etc. have made significant contribution to the research
                 of EA. In the past decade, a new evolutionary approach
                 named Genetic Network Programming (GNP) was proposed
                 and attracted more and more attention. GNP which is
                 based on the idea of Genetic Algorithm, also can evolve
                 itself and search in the solution domain of large scale
                 and finally find the (approximate) optimal solutions.
                 The unique character of GNP which make it very
                 different from other methods of EA is the use of the
                 data structure of directed graphs. Many research has
                 demonstrated that GNP can deal with complex problems in
                 the dynamical environments very efficiently and
                 effectively due to its graph based structure. As a
                 result, recently, GNP is being used in many different
                 areas such as data mining, extracting trading rules of
                 stock markets, elevator supervised control systems,
                 etc. and GNP has obtained outstanding results in all
                 the above fields. On the other hand, many research
                 shows that classical EAs such as GA, usually fail to
                 solve problems in dynamical environments. So, scholars
                 devote themselves to the research on the enhancement of
                 the architecture of EAs. For example, different memory
                 schemes storing historical information during evolution
                 and reusing them later are designed for EAs to solve
                 complex problems in dynamical environments.

                 So, the motivation of this research is designing memory
                 schemes for GNP in order to improve its performance
                 further in the dynamical environments. So, four
                 different memory schemes are proposed: GNP with rules,
                 GNP with reconstructed individuals, GNP with route
                 nodes and adaptive mutation in SARSA learning of GNP.
                 GNP with rules stores first-order information on GNP
                 rules and uses them to generate new individuals. GNP
                 with reconstructed individuals will stores the complete
                 node transitions which can guide the agent with much
                 more effectiveness and uses them to enhance the gene
                 structures of the worst individuals. GNP with route
                 nodes employs an indirect memory scheme which uses the
                 stored information associated with current
                 environments. The adaptive mutation using Q values to
                 evaluate node branches adjusts the mutation rates and
                 mutation directions for node branches and achieves the
                 balance between exploration and exploitation. In order
                 to measure the performance of the proposed
                 architectures, the benchmark of tile-world was used as
                 the simulation environments. The simulation results
                 show some improvements brought by the memory schemes to
                 conventional GNPs.",
}

Genetic Programming entries for Fengming Ye

Citations