TkbGBMLMoga               package:FKBL               R Documentation

_C_r_e_a_t_e_s _a _k_n_o_w_l_e_d_g_e _b_a_s_e

_D_e_s_c_r_i_p_t_i_o_n:

     This is the implementation of MOGA, Multi Objective Genetic
     Algorithm genetic method. Described in chapter 6, pages 134-139 at
     Ishibuchi et al. The three objectives of this algorithm are to
     minimize, the size of the rules, the number of rules and the error
     in training. The size is related with the number of consequents
     which are not marked as "no matter". This means that the actual
     variable labeled with "no matter", is totally independent from the
     actual rule. The objective of looking for rules with less
     dependant variables, is to produce more human readable rules, as
     this way it is easier to understand the relationship between the
     variables found by the algorithm. As there are there three
     different objectives, this algorithm creates a set of not
     dominated knowledge bases. It is an elitist algorithm, this means
     that the best solutions are stored apart from the actual
     population, so they are never lost. Periodically this elite
     population polutes the ordinary population, this way there is a
     balance between the evolution of the normal population and the
     elite driven evolution. The evolution of the normal population is
     an standard genetic driven algorithm, with crossing and mutation
     operations.

_U_s_a_g_e:

      TkbGBMLMoga(kB, gen=50, cross=0.8, muta=0.01, train, 
                             pobl=20, elite=5)

_A_r_g_u_m_e_n_t_s:

     Takes knowledge base, the number of generations, the  crossing and
     mutation probability, the train data, the initial population and
     the initial elite population.

      kB: The knowledge base to tweak.

     gen: The number of generations.

   cross: The cross probability up to 1.

    muta: The mutation probability up to 1.

   train: The train dataset.

    pobl: The initial population of the algorithm.

   elite: The size of the elite population.

_V_a_l_u_e:

     Returns the set of not dominated knowledge bases.

_S_o_u_r_c_e:

     \begin{itemize}

     *  Ishibuchi, H., Nakashima, T., Nii, M.

     *  "Classification and modeling with linguistic information
        granules." 

     *  Soft Computing Approaches to Linguistic Data Mining. 

     *  Springer-Verlag, 2003 \end{itemize}

_E_x_a_m_p_l_e_s:

      data(kB)
      data(trainA)
      TkbGBMLMoga(kB, 50, 0.8, 0.01, trainA, 20, 5)

